The passage of Hurricane Matthew through central and eastern North Carolina during October 7–9, 2016, brought heavy rainfall, which resulted in major flooding. More than 15 inches of rain was recorded in some areas. More than 600 roads were closed, including Interstates 95 and 40, and nearly 99,000 structures were affected by floodwaters. Immediately following the flooding, the U.S. Geological Survey documented 267 high-water marks, of which 254 were surveyed. North Carolina Emergency Management documented and surveyed 353 high-water marks. Using a subset of these highwater marks, six flood-inundation maps were created for hard-hit communities. Digital datasets of the inundation areas, study reach boundary, and water-depth rasters are available for download. In addition, peak gage-height data, peak streamflow data, and annual exceedance probabilities (in percent) were determined for 24 U.S. Geological Survey streamgages located near the heavily flooded communities.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171047","collaboration":"Prepared in cooperation with the Federal Emergency Management Agency","usgsCitation":"Musser, J.W., Watson, K.M., and Gotvald, A.J., 2017, Characterization of peak streamflows and flood inundation at selected areas in North Carolina following Hurricane Matthew, October 2016 (ver. 2.0, August 2017): U.S. Geological Survey Open-File Report 2017–1047, 23 p., https://doi.org/10.3133/ofr20171047.","productDescription":"Report: v, 23 p.; Data Release, Version History","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-085645","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":340658,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F75X276T","text":"USGS data release","description":"USGS data release","linkHelpText":"Flood inundation, flood depth, and high-water marks for selected areas in North Carolina from the October 2016 flood"},{"id":340659,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1047/ofr20171047.pdf","text":"Report","size":"4.02 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1047"},{"id":340657,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1047/coverthb3.jpg"},{"id":342197,"rank":4,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2017/1047/versionHist.txt","size":"2.31 MB","linkFileType":{"id":2,"text":"txt"}}],"country":"United States","state":"North Carolina, South Carolina","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -79.75,\n 34\n ],\n [\n -76.75,\n 34\n ],\n [\n -76.75,\n 36.116667\n ],\n [\n -79.75,\n 36.116667\n ],\n [\n -79.75,\n 34\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: October 2016; Version 1.1: June 2017; Version 2.0: August 2017","contact":"Director, South Atlantic Water Science Center
U.S. Geological Survey
720 Gracern Road
Stephenson Center, Suite 129
Columbia, SC 29210
We map the 385-kilometer (km) long surface trace of the right-lateral, strike-slip Denali Fault between the Totschunda-Denali Fault intersection in Alaska, United States and the village of Haines Junction, Yukon, Canada. In Alaska, digital elevation models based on light detection and ranging and interferometric synthetic aperture radar data enabled our fault mapping at scales of 1:2,000 and 1:10,000, respectively. Lacking such resources in Yukon, we developed new structure-from-motion digital photogrammetry products from legacy aerial photos to map the fault surface trace at a scale of 1:10,000 east of the international border. The section of the fault that we map, referred to as the Eastern Denali Fault, did not rupture during the 2002 Denali Fault earthquake (moment magnitude 7.9). Seismologic, geodetic, and geomorphic evidence, along with a paleoseismic record of past ground-rupturing earthquakes, demonstrate Holocene and contemporary activity on the fault, however. This map of the Eastern Denali Fault surface trace complements other data sets by providing an openly accessible digital interpretation of the location, length, and continuity of the fault’s surface trace based on the accompanying digital topography dataset. Additionally, the digitized fault trace may provide geometric constraints useful for modeling earthquake scenarios and related seismic hazard.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171049","usgsCitation":"Bender, A.M., and Haeussler, P.J., 2017, Eastern Denali Fault surface trace map, eastern Alaska and Yukon, Canada: U.S. Geological Survey Open-File Report 2017–1049, 10 p., https://doi.org/10.3133/ofr20171049.","productDescription":"iii, 10 p.","numberOfPages":"13","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-084514","costCenters":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"links":[{"id":438353,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7T151WC","text":"USGS data release","linkHelpText":"Eastern Denali Fault Surface Trace Map, Eastern Alaska and Adjacent Canada, 1978-2008"},{"id":422373,"rank":3,"type":{"id":15,"text":"Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_105646.htm","linkFileType":{"id":5,"text":"html"}},{"id":340824,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1049/coverthb.jpg"},{"id":340825,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1049/ofr20171049.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1049"}],"country":"Canada, United States","state":"Alaska, Yukon","otherGeospatial":"Denali Fault","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -147,\n 60\n ],\n [\n -135,\n 60\n ],\n [\n -135,\n 64\n ],\n [\n -147,\n 64\n ],\n [\n -147,\n 60\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Alaska Science Center
U.S. Geological Survey
4210 University Dr.
Anchorage, AK 99508
The U.S. Geological Survey Monthly Water Balance Model Futures Portal (https://my.usgs.gov/mows/) is a user-friendly interface that summarizes monthly historical and simulated future conditions for seven hydrologic and meteorological variables (actual evapotranspiration, potential evapotranspiration, precipitation, runoff, snow water equivalent, atmospheric temperature, and streamflow) at locations across the conterminous United States (CONUS).
The estimates of these hydrologic and meteorological variables were derived using a Monthly Water Balance Model (MWBM), a modular system that simulates monthly estimates of components of the hydrologic cycle using monthly precipitation and atmospheric temperature inputs. Precipitation and atmospheric temperature from 222 climate datasets spanning historical conditions (1952 through 2005) and simulated future conditions (2020 through 2099) were summarized for hydrographic features and used to drive the MWBM for the CONUS. The MWBM input and output variables were organized into an open-access database. An Open Geospatial Consortium, Inc., Web Feature Service allows the querying and identification of hydrographic features across the CONUS. To connect the Web Feature Service to the open-access database, a user interface—the Monthly Water Balance Model Futures Portal—was developed to allow the dynamic generation of summary files and plots based on plot type, geographic location, specific climate datasets, period of record, MWBM variable, and other options. Both the plots and the data files are made available to the user for download
Director, USGS Colorado Water Science Center
U.S. Geological Survey
Box 25046, Mail Stop 415
Denver, CO 80225
The occurrence of arsenic and uranium in groundwater at concentrations that exceed drinking-water standards is a concern because of the potential adverse effects on human health. Some early studies of arsenic occurrence in groundwater considered anthropogenic causes, but more recent studies have focused on sources of naturally occurring arsenic to groundwater, such as minerals within aquifer materials that are in contact with groundwater. Arsenic and uranium in groundwater in New England have been shown to have a strong association to the geologic setting and nearby streambed sediment concentrations. In New Hampshire and Massachusetts, arsenic and uranium concentrations greater than human-health benchmarks have shown distinct spatial patterns when related to the bedrock units mapped at the local scale.
The Connecticut Department of Public Health (DPH) reported that there are about 322,600 private wells in Connecticut serving approximately 823,000 people, or 23 percent of the State’s population. The State does not require that existing private wells be routinely tested for arsenic, uranium, or other contaminants; consequently, private wells are only sampled at the well owner’s discretion or when they are newly constructed. The U.S. Geological Survey (USGS), in cooperation with the DPH, completed an assessment in 2016 on the distribution of concentrations of arsenic and uranium in groundwater from bedrock in Connecticut. This report presents the major findings for arsenic and uranium concentrations from water samples collected from 2013 to 2015 from private wells.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171046","issn":"2331-1258","collaboration":"Prepared in cooperation with the Connecticut Department of Public Health","usgsCitation":"Flanagan, S.M., and Brown, C.J., 2017, Arsenic and uranium in private wells in Connecticut, 2013–15: U.S. Geological Survey Open-File Report 2017–1046; 8 p., https://doi.org/10.3133/ofr20171046.","productDescription":"Report: 8 p; Data Release","numberOfPages":"8","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-076719","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":340583,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7K935P5","text":"USGS data release","description":"USGS data release","linkHelpText":"Inventory of water-quality and geologic-setting data from 674 private wells in Connecticut, 2013-2015"},{"id":340579,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1046/coverthb.jpg"},{"id":340580,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1046/ofr20171046.pdf","text":"Report","size":"6.14 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1046"}],"country":"United 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\"}}]}","contact":"Director, New England Water Science Center
U.S. Geological Survey
331 Commerce Way, Suite 2
Pembroke, NH 03275
The U.S. Geological Survey (USGS) has estimated the use of water in the United States at 5-year intervals since 1950. This report describes the water-use categories and data elements used for the national water-use compilation conducted as part of the USGS National Water-Use Science Project. The report identifies sources of water-use information, provides standard methods and techniques for estimating water use at the county level, and outlines steps for preparing documentation for the United States, the District of Columbia, Puerto Rico, and the U.S. Virgin Islands.
As part of this USGS program to document water use on a national scale, estimates of water withdrawals for the categories of public supply, self-supplied domestic, industrial, irrigation, and thermoelectric power are prepared for each county in each State, District, or territory by using the guidelines in this report. County estimates of water withdrawals for aquaculture, livestock, and mining are prepared for each State by using a county-based national model, although water-use programs in each State or Water Science Center have the option of producing independent county estimates of water withdrawals for these categories. Estimates of water withdrawals and consumptive use for thermoelectric power will be aggregated to the county level for each State by the national project; additionally, irrigation consumptive use at the county level will also be provided, although study chiefs in each State have the option of producing independent county estimates of water withdrawals and consumptive use for these categories.
Estimates of deliveries of water from public supplies for domestic use by county also will be prepared for each State. As a result, total domestic water use can be determined for each State by combining self-supplied domestic withdrawals and public-supplied domestic deliveries. Fresh groundwater and surface-water estimates will be prepared for all categories of use, and saline groundwater and surface-water estimates by county will be prepared for the categories of public supply, industrial, mining, and thermoelectric power. Power production for thermoelectric power and irrigated acres by irrigation system type will be compiled. If data are available, reclaimed-wastewater use will be compiled for the public-supply, industrial, mining, thermoelectric-power, and irrigation categories.
Optional water-use categories are commercial, hydroelectric power, and wastewater treatment. Optional data elements are public-supply deliveries to commercial, industrial, and thermoelectric-power users; consumptive use (for categories other than thermoelectric power and irrigation); irrigation conveyance loss; and number of facilities. Aggregation of water-use data by stream basin (eight-digit hydrologic unit code) and principal aquifers also is optional.
Water-use data compiled by the States will be stored in the USGS Aggregate Water-Use Data System (AWUDS). This database is a comprehensive aggregated database designed to store mandatory and optional data elements. AWUDS contains several routines that can be used for quality assurance and quality control of the data, and AWUDS produces tables of water-use data from the previous compilations.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171029","collaboration":"National Water-Use Science Project","usgsCitation":"Bradley, M.W., comp., 2017, Guidelines for preparation of State water-use estimates for 2015: U.S. Geological Survey Open-File Report 2017–1029, 54 p., https://doi.org/10.3133/ofr20171029.","productDescription":"viii, 54 p.","numberOfPages":"66","onlineOnly":"Y","ipdsId":"IP-078880","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":340450,"rank":3,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1029/coverthb2.jpg"},{"id":340259,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1029/ofr20171029.pdf","text":"Report","size":"719 kB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017–1029"},{"id":340258,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1029/coverthb.jpg"}],"contact":"Director, Lower Mississippi-Gulf Water Science Center—Tennessee
640 Grassmere Park
Suite 100
Nashville, TN 37211
Shallow landslides along coastal bluffs frequently occur in the railway corridor between Seattle and Everett, Washington. These slides disrupt passenger rail service, both because of required track maintenance and because the railroad owner, Burlington Northern Santa Fe Railway, does not allow passenger travel for 48 hours after a disruptive landslide. Sound Transit, which operates commuter trains in the corridor, is interested in a decision-making tool to help preemptively cancel passenger railway service in dangerous conditions and reallocate resources to alternative transportation.
Statistical analysis showed that a majority of landslides along the Seattle-Everett Corridor are strongly correlated with antecedent rainfall, but that 21-37 percent of recorded landslide dates experienced less than 1 inch of precipitation in the 3 days preceding the landslide and less than 4 inches of rain in the 15 days prior to the preceding 3 days. We developed two empirical thresholds to identify precipitation conditions correlated with landslide occurrence. The two thresholds are defined as P3 = 2.16-0.44P15 and P3 = 2.16-0.22P32, where P3 is the cumulative precipitation in the 3 days prior to the considered date and P15 or P32 is the cumulative precipitation in the 15 days or 32 days prior to P3 (all measurements given in inches). The two thresholds, when compared to a previously developed threshold, quantitatively improve the prediction rate.
We also investigated rainfall intensity-duration (ID) thresholds to determine whether revision would improve identification of moderate-intensity, landslide-producing storms. New, optimized ID thresholds evaluate rainstorms lasting at least 12 hours and identify landslide-inducing storms that were typically missed by previously published ID thresholds. The main advantage of the ID thresholds appears when they are combined with recent-antecedent thresholds because rainfall conditions that exceed both threshold types are more likely to induce two or more landslides than conditions that exceed only one threshold type.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171039","collaboration":"Prepared in cooperation with Sound Transit","usgsCitation":"Scheevel, C.R., Baum, R.L., Mirus, B.B., and Smith, J.B., 2017, Precipitation thresholds for landslide occurrence near Seattle, Mukilteo, and Everett, Washington: U.S. Geological Survey Open-File Report 2017–1039, 51 p., https://doi.org/10.3133/ofr20171039.","productDescription":"vi, 51 p.","numberOfPages":"60","onlineOnly":"Y","ipdsId":"IP-082570","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":340454,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1039/coverthb.jpg"},{"id":340455,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1039/ofr20171039.pdf","text":"Report","size":"8.96 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1039"}],"country":"United States","state":"Washington","city":"Everett, Mukilteo, Seattle","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123,\n 48.3\n ],\n [\n -122,\n 48.3\n ],\n [\n -122,\n 47.3\n ],\n [\n -123,\n 47.3\n ],\n [\n -123,\n 48.3\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Geologic Hazards Science Center
U.S. Geological Survey
Box 25046, MS–966
Denver, CO 80225-0046
https://www.usgs.gov/centers/geohazards/
","tableOfContents":"We operated a bird banding station on the Point Loma peninsula in western San Diego County, California, during spring and summer from 2011 to 2015. The station was established in 2010 as part of a long-term monitoring program for neotropical migratory birds during spring migration and for breeding birds as part of the Monitoring Avian Productivity and Survivorship (MAPS) program.
During spring migration (April and May), 2011–15, we captured 1,760 individual birds of 54 species, 91 percent (1,595) of which were newly banded, fewer than 1 percent (3) of which were recaptures that were banded in previous years, and 9 percent (143 hummingbirds, 2 hawks, and 17 other birds) of which we released unbanded. We observed an additional 22 species that were not captured. Thirty-four individuals were captured more than once. Bird capture rate averaged 0.49 ± 0.07 captures per net-hour (range 0.41–0.56). Species richness per day averaged 6.87 ± 0.33. Cardellina pusilla (Wilson’s warbler) was the most abundant spring migrant captured, followed by Empidonax difficilis (Pacific-slope flycatcher), Vireo gilvus (warbling vireo), Zonotrichia leucophrys (white-crowned sparrow), and Selasphorus rufus (rufous hummingbird). Captures of white-crowned sparrow decreased, and captures of Pacific-slope flycatcher increased, over the 5 years of our study. Fifty-six percent of known-sex individuals were male and 44 percent were female. The peak number of new species arriving per day ranged from April 1 (2013-six species) to April 16 (2012-five species). A significant correlation was determined between the number of migrants captured each day per net-hour and the density of echoes on the Next-Generation Radar (NEXRAD) images across all 5 years, and in each year except 2014. NEXRAD radar imagery appears to be a useful tool for detecting pulses in migration.
Our results indicate that Point Loma provides stopover habitat during migration for 76 migratory species, including 20 species of conservation concern. Two of these species, Vireo bellii pusillus (least Bell’s vireo) and Empidonax traillii (willow flycatcher) are listed as State and (or) federally threatened or endangered.
Except for Archilochus alexandri (black-chinned hummingbird) and Setophaga occidentalis (hermit warbler), which arrived later during the migratory season in latter years of our study, median arrival dates for migratory species tended to be earlier each year or did not change across 5 years. Of the five most common migratory species, white-crowned sparrow and rufous hummingbird arrived earlier in latter years of the study, but Pacific-slope flycatcher, warbling vireo, and Wilson’s warbler median arrival dates were variable and showed no trend.
We captured 1,680 individuals of 66 species during the MAPS/breeding season (May through August) across the 5 years of our study, 72 percent (1,211) of which were newly banded, 10 percent (167) of which were recaptures, and 18 percent (302 hummingbirds and other birds that escaped prior to banding) of which we released unbanded. Bird capture rate averaged 0.65 ± 0.21 captures per net-hour (range 0.12–2.54). Species richness per day ranged from 9.80 ± 5.01 to 14.20 ± 4.57. Calypte anna (Anna’s hummingbird) was the most abundant breeding species captured, followed by Oreothlypis celata (orange-crowned warbler), Psaltriparus minimus (bushtit), Pipilo maculatus (spotted towhee), Thryomanes bewickii (Bewick’s wren), Melozone crissalis (California towhee), and Chamaea fasciata (wrentit). Fifty-one percent of known-sex captures were female, and 49 percent were male. Thirty-one percent of known-age captures were juveniles.
Populations of bushtits and orange-crowned warbler decreased significantly over 5 years. Anna’s hummingbird abundance was high for 4 years, and then decreased in 2015. Bewick’s wren and wrentit populations were highest in 2015. There was no obvious pattern in spotted towhee and California towhee abundance across 5 years. Annual breeding productivity for most species was low in 2014 and high in 2015. Bewick’s wren had the highest breeding productivity of the six most commonly captured species, followed by bushtit. Orange-crowned warbler had the lowest breeding productivity. Breeding productivity was a significant predictor of population size the next year for bushtit, but not for any other resident breeding species examined.
Adult survivorship was generally high from 2013 to 14, and low from 2014 to 15. Wrentits had the highest survivorship of the most common species captured, followed by California towhee and orange-crowned warbler. Adult survivorship was lowest for bushtits and spotted towhees. Adult survivorship was a significant predictor of population size for bushtits, but not for any other resident species examined.
Our monitoring results indicate that Point Loma provides breeding habitat for seven species of conservation concern. One of these species, the federally threatened Polioptila californica californica (California gnatcatcher), was documented breeding at the study site.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171042","collaboration":"Prepared in cooperation with Commander, Navy Region Southwest","usgsCitation":"Lynn, Suellen, Madden, M.C., and Kus, B.E., 2017, Monitoring breeding and migration of neotropical migratory birds at Point Loma, San Diego County, California, 5-year summary, 2011–15: U.S. Geological Survey Open-File Report 2017-1042, 119 p., https://doi.org/10.3133/ofr20171042.","productDescription":"iv, 119 p.","numberOfPages":"128","onlineOnly":"Y","ipdsId":"IP-079355","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":340506,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1042/coverthb.jpg"},{"id":340507,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1042/ofr20171042.pdf","text":"Report","size":"5.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1042"}],"country":"United States","state":"California","county":"San Diego County","otherGeospatial":"Point Loma","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.24781036376953,\n 32.66445129351451\n ],\n [\n -117.23575115203859,\n 32.66445129351451\n ],\n [\n -117.23575115203859,\n 32.67825116303079\n ],\n [\n -117.24781036376953,\n 32.67825116303079\n ],\n [\n -117.24781036376953,\n 32.66445129351451\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
https://www.werc.usgs.gov/
A theoretical basis and prototype numerical algorithm are provided that decompose regular time series of geomagnetic observations into three components: secular variation; solar quiet, and disturbance. Respectively, these three components correspond roughly to slow changes in the Earth’s internal magnetic field, periodic daily variations caused by quasi-stationary (with respect to the sun) electrical current systems in the Earth’s magnetosphere, and episodic perturbations to the geomagnetic baseline that are typically driven by fluctuations in a solar wind that interacts electromagnetically with the Earth’s magnetosphere. In contrast to similar algorithms applied to geomagnetic data in the past, this one addresses the issue of real time data acquisition directly by applying a time-causal, exponential smoother with “seasonal corrections” to the data as soon as they become available.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171037","usgsCitation":"Rigler, E.J., 2017, Time-causal decomposition of geomagnetic time series into secular variation, solar quiet, and disturbance signals: U.S. Geological Survey Open-File Report 2017–1037, 26 p., https://doi.org/10.3133/ofr20171037.","productDescription":"iv, 26 p.","numberOfPages":"31","onlineOnly":"Y","ipdsId":"IP-080216","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":340156,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1037/coverthb.jpg"},{"id":340157,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1037/ofr20171037.pdf","text":"Report","size":"3.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1037"}],"contact":"Director, Geologic Hazards Science Center
U.S. Geological Survey
Box 25046, MS–966
Denver, CO 80225-0046
https://www.usgs.gov/centers/geohazards/
","tableOfContents":"This study was undertaken to determine if the U.S. Geological Survey’s process for conducting mineral resource assessments on Earth can be applied to asteroids. Successful completion of the assessment, using water and iron resources to test the workflow, has resulted in identification of the minimal adjustments required to conduct full resource assessments beyond Earth. We also identify the types of future studies that would greatly reduce uncertainties in an actual future assessment. Whereas this is a feasibility study and does not include a complete and robust analysis of uncertainty, it is clear that the water and metal resources in near-Earth asteroids are sufficient to support humanity should it become a fully space-faring species.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171041","productDescription":"iii, 28 p.","onlineOnly":"Y","ipdsId":"IP-080222","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":340103,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1041/coverthb2.jpg"},{"id":340091,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1041/ofr20171041.pdf","text":"Report","size":"525 KB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1041"}],"contact":"Astrogeology Science Center
U.S. Geological Survey
2255 N. Gemini Dr.
Flagstaff, AZ 86001
https://astrogeology.usgs.gov/
Bacteria-driven restrictions and (or) advisories on swimming at beaches in Presque Isle State Park (PISP), Erie, Pennsylvania, can occur during the summer months. One of the suspected sources of bacteria is sediment. A terrestrial sediment source to the west of PISP is Walnut Creek, which discharges to Lake Erie about 8.5 kilometers southwest of PISP Beach 1. On June 24, June 25, August 18, and August 19, 2015, synoptic surveys were conducted by the U.S. Geological Survey, in cooperation with the Pennsylvania Sea Grant, in Lake Erie between Walnut Creek and PISP Beach 1 to characterize the water-current velocity and direction to determine whether sediment from Walnut Creek could be affecting the PISP beaches. Water-quality data (temperature, specific conductance, and turbidity) were collected in conjunction with the synoptic surveys in June. Water-quality data (Escherichia coli [E. coli] bacteria, temperature, and turbidity) were collected about a meter from the shore (nearshore) on June 24, August 19, and after a precipitation event on August 11, 2015. Additionally, suspended sediment was collected nearshore on June 24 and August 11, 2015. Samples collected near Walnut Creek during all three bacterial sampling events contained higher counts than other samples. Counts steadily decreased from west to east, then increased about 1–2 kilometers from PISP Beach 1; however, this study was not focused on examining other potential sources of bacteria.
The Velocity Mapping Toolbox (VMT) was used to process the water-current synoptic surveys, and the results were visualized within ArcMap. For the survey accomplished on June 24, 2015, potential paths a particle could take between Walnut Creek and PSIP Beach 1 if conditions remained steady over a number of hours were visualized. However, the water-current velocity and direction were variable from one day to the other, indicating this was likely an unrealistic assumption for the study area. This analysis was not accomplished for the other surveys due to unsteady lake conditions encountered on June 25 and August 18, and reduced quality of the survey on August 19.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161206","collaboration":"Prepared in cooperation with the Pennsylvania Sea Grant","usgsCitation":"Hittle, Elizabeth, 2017, Longshore water-current velocity and the potential for transport of contaminants—A pilot study in Lake Erie from Walnut Creek to Presque Isle State Park beaches, Erie, Pennsylvania, June and August 2015: U.S. Geological Survey Open-File Report 2016–1206, 126 p., https://doi.org/10.3133/ofr20161206.","productDescription":"Report: x, 126 p.; Appendixes 2-1 - 2-3; Data Release","numberOfPages":"140","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-077254","costCenters":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"links":[{"id":438368,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7KP808D","text":"USGS data release","linkHelpText":"Data Collected in Support of the Longshore Water-Current Velocity and the Potential for Transport of Contaminants pilot study in Lake Erie from Walnut Creek to Presque Isle State Park Beaches, Erie, Pennsylvania"},{"id":339295,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://dx.doi.org/10.5066/F7KP808D","text":"USGS data release","description":"USGS data release","linkHelpText":"Longshore Water-Current Velocity and the Potential for Transport of Contaminants: A pilot study in Lake Erie from Walnut Creek to Presque Isle State Park Beaches, Erie, Pennsylvania, June and August 2015"},{"id":339174,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1206/coverthb2.jpg"},{"id":339175,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1206/ofr20161206.pdf","text":"Report","size":"28.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1206"},{"id":339176,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2016/1206/ofr20161206_appendix2-1.csv","text":"Appendix 2-1","size":"1.29 KB","linkFileType":{"id":7,"text":"csv"},"linkHelpText":"- Regression Statistics for Figures 23-25"},{"id":339178,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2016/1206/ofr20161206_appendix2-3.csv","text":"Appendix 2-3","size":"1.25 KB","linkFileType":{"id":7,"text":"csv"},"linkHelpText":"- Regression Statistics for Figures 23-25"},{"id":339177,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2016/1206/ofr20161206_appendix2-2.csv","text":"Appendix 2-2","size":"1.29 KB","linkFileType":{"id":7,"text":"csv"},"linkHelpText":"- Regression Statistics for Figures 23-25"}],"country":"United States","state":"Pennsylvania","city":"Erie","otherGeospatial":"Lake Erie, Presque Isle State Park, Walnut Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -80.25,\n 42.06667\n ],\n [\n -80.13333,\n 42.06667\n ],\n [\n -80.13333,\n 42.13333\n ],\n [\n -80.25,\n 42.13333\n ],\n [\n -80.25,\n 42.06667\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Pennsylvania Water Science Center
U.S. Geological Survey
215 Limekiln Road
New Cumberland, PA 17070
https://pa.water.usgs.gov/
The Piedmont Thrust Fault, herein referred to as the Piedmont Reverse Fault (PRF), is a splay of the Hayward Fault that trends through a highly populated area of the City of Oakland, California (fig. 1A). Although the PRF is unlikely to generate a large-magnitude earthquake, slip on the PRF or high-amplitude seismic energy traveling along the PRF may cause considerable damage during a large earthquake on the Hayward Fault. Thus, it is important to determine the exact location, geometry (particularly dip), and lateral extent of the PRF within the densely populated Oakland area. In the near surface, the PRF juxtaposes Late Cretaceous sandstone (of the Franciscan Complex Novato Quarry terrane of Blake and others, 1984) and an older Pleistocene alluvial fan unit along much of its mapped length (fig. 1B; Graymer and others, 1995). The strata of the Novato Quarry unit vary greatly in strike (NW, NE, and E), dip direction (NE, SW, E, and NW), dip angle (15° to 85°), and lithology (shale and sandstone), and the unit has been intruded by quartz diorite in places. Thus, it is difficult to infer the structure of the fault, particularly at depth, with conventional seismic reflection imaging methods. To better determine the location and shallow-depth geometry of the PRF, we used high-resolution seismic imaging methods described by Catchings and others (2014). These methods involve the use of coincident P-wave (compressional wave) and S-wave (shear wave) refraction tomography and reflection data, from which tomographic models of P- and S-wave velocity and P-wave reflection images are developed. In addition, the coincident P-wave velocity (VP) and S-wave velocity (VS) data are used to develop tomographic models of VP/VS ratios and Poisson’s ratio, which are sensitive to shallow-depth faulting and groundwater. In this study, we also compare measurements of Swave velocities determined from surface waves with those determined from refraction tomography. We use the combination of seismic methods to infer the fault location, dip, and the National Earthquake Hazards Reduction Program (NEHRP) site classification along the seismic profile. Our seismic study is a smaller part of a larger study of the PRF by Trench and others (2016).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161123","usgsCitation":"Catchings, R.D., Goldman, M.R., Trench, David, Buga, Michael, Chan, J.H., Criley, C.J., and Strayer, L.M., 2017, Shallow-depth location and geometry of the Piedmont Reverse splay of the Hayward Fault, Oakland, California: U.S. Geological Survey Open-File Report 2016–1123, 22 p., https://dx.doi.org/10.3133/ofr20161123.","productDescription":"iii, 22 p.","onlineOnly":"Y","ipdsId":"IP-073235","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":339832,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1123/ofr20161123.pdf","text":"Report","size":"12.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1123"},{"id":339831,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1123/coverthb.jpg"}],"country":"United States","state":"California","city":"Oakland","otherGeospatial":"Hayward Fault","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.24727630615236,\n 37.784554114444994\n ],\n [\n -122.16590881347656,\n 37.784554114444994\n ],\n [\n -122.16590881347656,\n 37.83771661984569\n ],\n [\n -122.24727630615236,\n 37.83771661984569\n ],\n [\n -122.24727630615236,\n 37.784554114444994\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Earthquake Science Center—Menlo Park, Calif. Office
U.S. Geological Survey
345 Middlefield Road, MS 977
Menlo Park, CA 94025
http://earthquake.usgs.gov/
Several U.S. Department of Interior (DOI) agencies are concerned about the cumulative effects of groundwater development on groundwater resources managed by, and other groundwater resources of interest to, these agencies in Snake Valley and surrounding areas. The new water uses that potentially concern the DOI agencies include 12 water-right applications filed in 2005, totaling approximately 8,864 acre-feet per year. To date, only one of these applications has been approved and partially developed. In addition, the DOI agencies are interested in the potential effects of three new water-right applications (UT 18-756, UT 18-758, and UT 18-759) and one water-right change application (UT a40687), which were the subject of a water-right hearing on April 19, 2016.
This report presents a hydrogeologic analysis of areas in and around Snake Valley to assess potential effects of existing and future groundwater development on groundwater resources, specifically groundwater discharge sites, of interest to the DOI agencies. A previously developed steady-state numerical groundwater-flow model was modified to transient conditions with respect to well withdrawals and used to quantify drawdown and capture (withdrawals that result in depletion) of natural discharge from existing and proposed groundwater withdrawals. The original steady-state model simulates and was calibrated to 2009 conditions. To investigate the potential effects of existing and proposed groundwater withdrawals on the groundwater resources of interest to the DOI agencies, 10 withdrawal scenarios were simulated. All scenarios were simulated for periods of 5, 10, 15, 30, 55, and 105 years from the start of 2010; additionally, all scenarios were simulated to a new steady state to determine the ultimate long-term effects of the withdrawals. Capture maps were also constructed as part of this analysis. The simulations used to develop the capture maps test the response of the system, specifically the reduction of natural discharge, to future stresses at a point in the area represented by the model. In this way, these maps can be used as a tool to determine the source of water to, and potential effects at specific areas from, future well withdrawals.
Downward trends in water levels measured in wells indicate that existing groundwater withdrawals in Snake Valley are affecting water levels. The numerical model simulates similar downward trends in water levels; simulated drawdowns in the model, however, are generally less than observed water-level declines. At the groundwater discharge sites of interest to the DOI agencies, simulated drawdowns from existing well withdrawals (projected into the future) range from 0 to about 50 feet. Following the addition of the proposed withdrawals, simulated drawdowns at some sites increase by 25 feet. Simulated drawdown resulting from the proposed withdrawals began in as few as 5 years after 2014 at several of the sites. At the groundwater discharge sites of interest to the DOI agencies, simulated capture of natural discharge resulting from the existing withdrawals ranged from 0 to 87 percent. Following the addition of the proposed withdrawals, simulated capture at several of the sites reached 100 percent, indicating that groundwater discharge at that site would cease. Simulated capture following the addition of the proposed withdrawals increased in as few as 5 years after 2014 at several of the sites.
Director, Utah Water Science Center
U.S. Geological Survey
2329 West Orton Circle
Salt Lake City, UT 84119-2047
801 908-5000
http://ut.water.usgs.gov/
Evidence indicates that competition with invasive barred owls (Strix varia) is causing rapid declines in populations of northern spotted owls (S. occidentalis caurina), and that the long-term persistence of spotted owls may be in question without additional management intervention. A pilot study in California showed that removal of barred owls in combination with habitat conservation may be able to slow or even reverse population declines of spotted owls at local scales, but it remains unknown whether similar results can be obtained in areas with different forest conditions and a greater density of barred owls. In 2015, we implemented a before-after-control-impact (BACI) experimental design on three study areas in Oregon and Washington with at least 20 years of pre-treatment demographic data on spotted owls to determine if removal of barred owls can improve localized population trends of spotted owls. Here, we report on research accomplishments and preliminary results from the first 21 months (March 2015–December 2016) of the planned 5-year experiment.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171040","collaboration":"Prepared in cooperation with U.S. Fish and Wildlife Service, Bureau of Land Management, and U.S. Forest Service","usgsCitation":"Wiens, J.D., Dugger, K.M., Lewicki, K.E., and Simon, D.C., 2017, Effects of experimental removal of barred owls on population demography of northern spotted owls in Washington and Oregon—2016 progress report: U.S. Geological Survey Open-File Report 2017-1040, 23 p., https://doi.org/10.3133/ofr20171040.","productDescription":"iv, 23 p.","numberOfPages":"32","onlineOnly":"Y","ipdsId":"IP-084885","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":339711,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1040/coverthb.jpg"},{"id":339712,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1040/ofr20171040.pdf","text":"Report","size":"5.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1040"}],"country":"United States","state":"Oregon, Washington","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.94775390625,\n 42.4234565179383\n ],\n [\n -120.10253906249999,\n 42.4234565179383\n ],\n [\n -120.10253906249999,\n 47.945786463687185\n ],\n [\n -123.94775390625,\n 47.945786463687185\n ],\n [\n -123.94775390625,\n 42.4234565179383\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Forest and Rangeland Ecosystem Science Center
U.S. Geological Survey
777 NW 9th St., Suite 400
Corvallis, Oregon 97330
http://fresc.usgs.gov/
Topographic elevation data collected with airborne light detection and ranging (lidar) can be used to analyze short- and long-term changes to beach and dune systems. Analysis of multiple lidar datasets at Dauphin Island, Alabama, revealed systematic, island-wide elevation differences on the order of 10s of centimeters (cm) that were not attributable to real-world change and, therefore, were likely to represent systematic sampling offsets. These offsets vary between the datasets, but appear spatially consistent within a given survey. This report describes a method that was developed to identify and correct offsets between lidar datasets collected over the same site at different times so that true elevation changes over time, associated with sediment accumulation or erosion, can be analyzed.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171031","usgsCitation":"Thompson, D.M., Dalyander, P.S., Long, J.W., and Plant, N.G., 2017, Correction of elevation offsets in multiple co-located lidar datasets: U.S. Geological Survey Open-File Report 2017–1031, 10 p., https://doi.org/10.3133/ofr20171031. ","productDescription":"iv, 10 p.","numberOfPages":"15","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-079032","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":339143,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1031/ofr20171031.pdf","text":"Report","size":"546 KB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1031"},{"id":339142,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1031/coverthb.jpg"}],"country":"United States","state":"Alabama","otherGeospatial":"Dauphin Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.21369171142578,\n 30.217541849095714\n ],\n [\n -88.06949615478516,\n 30.217541849095714\n ],\n [\n -88.06949615478516,\n 30.28575280701959\n ],\n [\n -88.21369171142578,\n 30.28575280701959\n ],\n [\n -88.21369171142578,\n 30.217541849095714\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"St. Petersburg Coastal and Marine Science Center
U.S. Geological Survey
600 4th Street South
St. Petersburg, FL 33701
http://coastal.er.usgs.gov/
Investigations of coastal change at Fire Island, New York (N.Y.), sought to characterize sediment budgets and determine geologic framework controls on coastal processes. Nearshore sediment thickness is critical for assessing coastal system sediment availability, but it is largely unquantified due to the difficulty of conducting geological or geophysical surveys across the nearshore. This study used an amphibious vessel to acquire chirp subbottom profiles. These profiles were used to characterize nearshore geology and provide an assessment of nearshore sediment volume. Two resulting sediment-thickness maps are provided: total Holocene sediment thickness and the thickness of the active shoreface. The Holocene sediment section represents deposition above the maximum flooding surface that is related to the most recent marine transgression. The active shoreface section is the uppermost Holocene sediment, which is interpreted to represent the portion of the shoreface thought to contribute to present and future coastal behavior. The sediment distribution patterns correspond to previously defined zones of erosion, accretion, and stability along the island, demonstrating the importance of sediment availability in the coastal response to storms and seasonal variability. The eastern zone has a thin nearshore sediment thickness, except for an ebb-tidal deposit at the wilderness breach caused by Hurricane Sandy. Thicker sediment is found along a central zone that includes shoreface-attached sand ridges, which is consistent with a stable or accretional coastline in this area. The thickest overall Holocene section is found in the western zone of the study, where a thicker lower section of Holocene sediment appears related to the westward migration of Fire Island Inlet over several hundred years.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171024","usgsCitation":"Locker, S.D., Miselis, J.L., Buster, N.A., Hapke, C.J., Wadman, H.M., McNinch, J.E., Forde, A.S., and Stalk, C.A., 2017, Nearshore sediment thickness, Fire Island, New York: U.S. Geological Survey Open-File Report 2017–1024, 21 p., https://doi.org/10.3133/ofr20171024.","productDescription":"vii, 21 p.","numberOfPages":"29","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-079609","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":338703,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1024/ofr20171024.pdf","text":"Report","size":"6.61 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1024"},{"id":338702,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1024/coverthb.jpg"}],"country":"United States","state":"New York","otherGeospatial":"Fire Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -72.87986755371094,\n 40.77534183237267\n ],\n [\n -72.87986755371094,\n 40.77534183237267\n ],\n [\n -72.87986755371094,\n 40.77534183237267\n ],\n [\n -72.87986755371094,\n 40.77534183237267\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -72.87574768066406,\n 40.77534183237267\n ],\n [\n -73.27743530273438,\n 40.65563874006118\n ],\n [\n -73.24447631835938,\n 40.59674926086908\n ],\n [\n -72.84210205078125,\n 40.7202010588415\n ],\n [\n -72.87574768066406,\n 40.77534183237267\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, St. Petersburg Coastal and Marine Science Center
U.S. Geological Survey
600 4th Street South
St. Petersburg, FL 33701
https://coastal.er.usgs.gov/
This report summarizes the results of three-dimensional (3-D) resistivity inversion simulations that were performed to account for local 3-D distortion of the electric field in the presence of 3-D regional structure, without any a priori information on the actual 3-D distribution of the known subsurface geology. The methodology used a 3-D geologic model to create a 3-D resistivity forward (“known”) model that depicted the subsurface resistivity structure expected for the input geologic configuration. The calculated magnetotelluric response of the modeled resistivity structure was assumed to represent observed magnetotelluric data and was subsequently used as input into a 3-D resistivity inverse model that used an iterative 3-D algorithm to estimate 3-D distortions without any a priori geologic information. A publicly available inversion code, WSINV3DMT, was used for all of the simulated inversions, initially using the default parameters, and subsequently using adjusted inversion parameters. A semiautomatic approach of accounting for the static shift using various selections of the highest frequencies and initial models was also tested. The resulting 3-D resistivity inversion simulation was compared to the “known” model and the results evaluated. The inversion approach that produced the lowest misfit to the various local 3-D distortions was an inversion that employed an initial model volume resistivity that was nearest to the maximum resistivities in the near-surface layer.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171007","usgsCitation":"Rodriguez, B.D., 2017, Semiautomatic approaches to account for 3-D distortion of the electric field from local, near-surface structures in 3-D resistivity inversions of 3-D regional magnetotelluric data: U.S. Geological Survey Open-File Report 2017–1007, 25 p., https://doi.org/10.3133/ofr20171007.","productDescription":"iii, 25 p.","numberOfPages":"29","onlineOnly":"Y","ipdsId":"IP-068212","costCenters":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":338862,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1007/coverthb.jpg"},{"id":338863,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1007/ofr20171007.pdf","text":"Report","size":"20.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1007"}],"contact":"Director, Crustal Geophysics and Geochemistry Science Center
U.S. Geological Survey
Box 25046, MS 964
Denver, CO 80225
The use of Landsat satellite imagery for global agricultural monitoring began almost immediately after the launch of Landsat 1 in 1972, making agricultural monitoring one of the longest-standing operational applications for the Landsat program. More recently, Landsat imagery has been used in domestic agricultural applications as an input for field-level production management. The enactment of the U.S. Geological Survey’s free and open data policy in 2008 and the launch of Landsat 8 in 2013 have both influenced agricultural applications. This report presents two primary sets of case studies on the applications and benefits of Landsat imagery use in agriculture. The first set examines several operational applications within the U.S. Department of Agriculture (USDA) and the second focuses on private sector applications for agronomic management.
Information on the USDA applications is provided in the U.S. Department of Agriculture Uses of Landsat Imagery for Global and Domestic Agricultural Monitoring section of the report in the following subsections:
Private sector applications using Landsat imagery for agricultural management are discussed in the Landsat Imagery Use and Benefits in Field-Level Agricultural Production Management section of the report in the following subsections:
Director, Fort Collins Science Center
U.S. Geological Survey
2150 Centre Ave., Bldg. C
Fort Collins, CO 80526-8118
The salt marshes of Jamaica Bay, managed by the New York City Department of Parks & Recreation and the Gateway National Recreation Area of the National Park Service, serve as a recreational outlet for New York City residents, mitigate flooding, and provide habitat for critical wildlife species. Hurricanes and extra-tropical storms have been recognized as one of the critical drivers of coastal wetland morphology due to their effects on hydrodynamics and sediment transport, deposition, and erosion processes. However, the magnitude and mechanisms of hurricane effects on sediment dynamics and associated coastal wetland morphology in the northeastern United States are poorly understood. In this study, the depth-averaged version of the Delft3D modeling suite, integrated with field measurements, was utilized to examine the effects of Hurricane Sandy and future potential hurricanes on salt marsh morphology in Jamaica Bay, New York City. Hurricane Sandy-induced wind, waves, storm surge, water circulation, sediment transport, deposition, and erosion were simulated by using the modeling system in which vegetation effects on flow resistance, surge reduction, wave attenuation, and sedimentation were also incorporated. Observed marsh elevation change and accretion from a rod surface elevation table and feldspar marker horizons and cesium-137- and lead-210-derived long-term accretion rates were used to calibrate and validate the wind-waves-surge-sediment transport-morphology coupled model.
The model results (storm surge, waves, and marsh deposition and erosion) agreed well with field measurements. The validated modeling system was then used to detect salt marsh morphological change due to Hurricane Sandy across the entire Jamaica Bay over the short-term (for example, 4 days and 1 year) and long-term (for example, 5 and 10 years). Because Hurricanes Sandy (2012) and Irene (2011) were two large and destructive tropical cyclones which hit the northeast coast, the validated coupled model was run to predict the effects of Sandy-like and Irene-like hurricanes with different storm tracks and wind intensities on wetland morphology in Jamaica Bay. Model results indicate that, in Jamaica Bay salt marshes, the morphological changes (greater than 5 millimeters [mm] determined by the long-term marsh accretion rate) caused by Hurricane Sandy were complex and spatially heterogeneous. Most of the erosion (5–40 mm) and deposition (5–30 mm) were mainly characterized by fine sand for channels and bay bottoms and by mud for marsh areas. Hurricane Sandy-generated deposition and erosion were generated locally. The storm-induced net sediment input through Rockaway Inlet was only about 1 percent of the total amount of the sediment reworked by the hurricane. Salt marshes inside the western part of the bay showed erosion overall while marshes inside the eastern part showed deposition from Hurricane Sandy. Model results indicated that most of the marshes could recover from Hurricane Sandy-induced erosion after 1 year and demonstrated continued marsh accretion after the hurricane over the course of long simulation periods although the effect (accretion) was diminished. Local waves and currents generated by Hurricane Sandy appeared to play a critical role in sediment transport and associated wetland morphological change in Jamaica Bay. Hypothetical hurricanes, depending on their track and intensity, cause variable responses in spatial patterns of sediment deposition and erosion compared to simulations without the hurricane. In general, hurricanes passing west of the Jamaica Bay estuary appear to be more destructive to the salt marshes than those passing the east. Consequently, marshes inside the western part of the bay were likely to be more vulnerable to hurricanes than marshes inside the eastern part of the bay.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171016","usgsCitation":"Wang, H., Chen, Q., Hu, K., Snedden, G.A., Hartig, E.K., Couvillion, B.R., Johnson, C.L., and Orton, P.M., 2017, Numerical modeling of the effects of Hurricane Sandy and potential future hurricanes on spatial patterns of salt marsh morphology in Jamaica Bay, New York City: U.S. Geological Survey Open-File Report 2017–1016, 43 p., https://doi.org/10.3133/ofr20171016.","productDescription":"vii, 43 p.","numberOfPages":"56","onlineOnly":"Y","ipdsId":"IP-079827","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":338515,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1016/coverthb.jpg"},{"id":338516,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1016/ofr20171016.pdf","text":"Report","size":"30.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017–1016"}],"country":"United States","state":"New York","city":"New York City","otherGeospatial":"Jamaica Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -73.95687103271484,\n 40.539112438263516\n ],\n [\n -73.72684478759766,\n 40.539112438263516\n ],\n [\n -73.72684478759766,\n 40.658503716866974\n ],\n [\n -73.95687103271484,\n 40.658503716866974\n ],\n [\n -73.95687103271484,\n 40.539112438263516\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"
Director, Wetland and Aquatic Research Center
U.S. Geological Survey
7920 NW 71st Street
Gainesville, FL 32653
https://www.usgs.gov/centers/wetland-and-aquatic-research-center-warc
","tableOfContents":"Borehole geophysical logs and thermal imaging data were collected by the U.S. Geological Survey near the Hemphill Road TCE (trichloroethylene) National Priorities List Superfund site near Gastonia, North Carolina, during August 2014 through February 2015. In an effort to assist the U.S. Environmental Protection Agency in the development of a conceptual groundwater model for the assessment of current contaminant distribution and future migration of contaminants, surface geological mapping and borehole geophysical log and thermal imaging data collection, which included the delineation of more than 600 subsurface features (primarily fracture orientations), was completed in five open borehole wells and two private supply bedrock wells. In addition, areas of possible groundwater discharge within a nearby creek downgradient of the study site were determined based on temperature differences between the stream and bank seepage using thermal imagery.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171017","issn":"2331-1258","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency Region 4 Superfund Section","usgsCitation":"Antolino, D.J., and Chapman, M.J., 2017, Geophysical logging and thermal imaging near the Hemphill Road TCE National Priorities List Superfund site near Gastonia, North Carolina (ver. 1.1, March 2017): U.S. Geological Survey Open-File Report 2017–1017, 47 p., https://doi.org/10.3133/ofr20171017.","productDescription":"Report: v, 47 p.; Data Release","numberOfPages":"57","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-079978","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":337458,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1017/coverthb2.jpg"},{"id":337459,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1017/ofr20171017.pdf","text":"Report","size":"19.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1017"},{"id":337460,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F71R6NPM","text":"USGS data release ","description":"USGS data release","linkHelpText":"Geophysical logging and thermal imaging at the Hemphill Road TCE NPL Superfund site near Gastonia, North Carolina"},{"id":338827,"rank":4,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2017/1017/versionHist.txt","linkFileType":{"id":2,"text":"txt"}}],"country":"United States","state":"North Carolina","county":"Gaston County","city":"Gastonia","otherGeospatial":"Hemphill Road trichloroethylene National Priorities List Superfund site","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-81.4535,35.4201],[-81.2581,35.4132],[-81.0069,35.4038],[-80.9549,35.4006],[-80.9554,35.3925],[-80.9632,35.3901],[-80.9761,35.3828],[-80.9806,35.3823],[-80.9846,35.3822],[-80.9868,35.38],[-80.9844,35.3695],[-80.9776,35.3646],[-80.9742,35.3642],[-80.9697,35.3669],[-80.9669,35.3688],[-80.9647,35.3738],[-80.9625,35.3756],[-80.9597,35.3756],[-80.9563,35.3738],[-80.9505,35.3675],[-80.9432,35.3658],[-80.9296,35.3636],[-80.9268,35.3627],[-80.9285,35.3614],[-80.9374,35.3572],[-80.9442,35.3521],[-80.9537,35.3521],[-80.9593,35.3489],[-80.9656,35.3506],[-80.9706,35.3501],[-80.9818,35.3446],[-80.984,35.3373],[-80.9823,35.3341],[-80.9805,35.3287],[-80.9844,35.3237],[-80.9894,35.3205],[-80.9938,35.3132],[-80.9961,35.3113],[-81.0022,35.3045],[-81.0033,35.3017],[-81.0105,35.2944],[-81.0133,35.293],[-81.0143,35.2876],[-81.0152,35.2685],[-81.0139,35.2585],[-81.0082,35.2509],[-81.012,35.2349],[-81.0113,35.2309],[-81.0129,35.2231],[-81.0071,35.2109],[-81.0054,35.2055],[-81.0064,35.1973],[-81.0063,35.1923],[-81.0046,35.1864],[-81.0045,35.1814],[-81.0049,35.1728],[-81.0088,35.165],[-81.0076,35.1569],[-81.0109,35.1532],[-81.0176,35.1536],[-81.0238,35.1486],[-81.0448,35.1494],[-81.0682,35.1507],[-81.1814,35.1568],[-81.2141,35.1586],[-81.3277,35.1637],[-81.3163,35.1906],[-81.3209,35.2609],[-81.355,35.2796],[-81.3548,35.2946],[-81.3594,35.3022],[-81.3675,35.314],[-81.3659,35.3181],[-81.3565,35.3309],[-81.3986,35.3531],[-81.4535,35.4201]]]},\"properties\":{\"name\":\"Gaston\",\"state\":\"NC\"}}]}","edition":"Version 1.0: Originally posted March 27, 2017; Version 1.1: March 30, 2017","contact":"Director, South Atlantic Water Science Center
U.S. Geological Survey
720 Gracern Road
Stephenson Center, Suite 129
Columbia, SC 29210
https://www2.usgs.gov/water/southatlantic/
County-level estimates of nitrogen and phosphorus inputs from animal manure for the conterminous United States were calculated from animal population inventories in the 2007 and 2012 Census of Agriculture, using previously published methods. These estimates of non-point nitrogen and phosphorus inputs from animal manure were compiled in support of the U.S. Geological Survey’s National Water-Quality Assessment Project of the National Water Quality Program and are needed to support national-scale investigations of stream and groundwater water quality. The estimates published in this report are comparable with older estimates which can be compared to show changes in nitrogen and phosphorus inputs from manure over time.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171021","issn":"2331-1258","usgsCitation":"Gronberg, J.M., and Arnold, T.L., 2017, County-level estimates of nitrogen and phosphorus from animal manure for the conterminous United States, 2007 and 2012: U.S. Geological Survey Open-File Report 2017–1021, 6 p., https://doi.org/10.3133/ofr20171021.","productDescription":"iii, 6 p.","numberOfPages":"14","onlineOnly":"Y","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":438408,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7X34VMZ","text":"USGS data release","linkHelpText":"County-level estimates of nitrogen and phosphorus from animal manure (2007 and 2012) and 30-meter-resolution grid of counties (2010) for the conterminous United States"},{"id":338286,"rank":3,"type":{"id":30,"text":"Data 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\"}}]}\n","contact":"Bathymetric, topographic, and grain-size data were collected in May 2009 along a 33-mi reach of the Colorado River in Grand Canyon National Park, Arizona. The study reach is located from river miles 29 to 62 at the confluence of the Colorado and Little Colorado Rivers. Channel bathymetry was mapped using multibeam and singlebeam echosounders, subaerial topography was mapped using ground-based total-stations, and bed-sediment grain-size data were collected using an underwater digital microscope system. These data were combined to produce digital elevation models, spatially variable estimates of digital elevation model uncertainty, georeferenced grain-size data, and bed-sediment distribution maps. This project is a component of a larger effort to monitor the status and trends of sand storage along the Colorado River in Grand Canyon National Park. This report documents the survey methods and post-processing procedures, digital elevation model production and uncertainty assessment, and procedures for bed-sediment classification, and presents the datasets resulting from this study.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171030","collaboration":"Prepared in cooperation with Northern Arizona University","usgsCitation":"Kaplinski, M., Hazel, J.E., Jr., Grams, P.E., Kohl, Keith, Buscombe, D.D., and Tusso, R.B., 2017, Channel mapping river miles 29–62 of the Colorado River in Grand Canyon National Park, Arizona, May 2009: U.S. Geological Survey Open-File Report 2017–1030, 35 p., https://doi.org/10.3133/ofr20171030.","productDescription":"vi, 35 p.","onlineOnly":"Y","ipdsId":"IP-079813","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":438410,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7930RCG","text":"USGS data release","linkHelpText":"Channel Mapping of the Colorado River in Grand Canyon National Park, Arizona - May 2009, river miles 29 to 62Data"},{"id":338081,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1030/coverthb.jpg"},{"id":338082,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1030/ofr20171030.pdf","text":"Report","size":"2.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1030"},{"id":350633,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://dx.doi.org/10.5066/F7930RCG","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Channel Mapping of the Colorado River in Grand Canyon National Park, Arizona, May 2009, river miles 29 to 62—Data"}],"country":"United States","state":"Arizona","otherGeospatial":"Colorado River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.7,\n 36.541667\n ],\n [\n -112,\n 36.541667\n ],\n [\n -112,\n 36.125\n ],\n [\n -111.7,\n 36.125\n ],\n [\n -111.7,\n 36.541667\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"GCMRC Staff, Southwest Biological Science Center
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Grand Canyon Monitoring and Research Center
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Flagstaff, Arizona 86001
https://www.gcmrc.gov/
A vast array of chemical compounds are in wide commercial use in the United States, and the potential ecological and human-health effect of exposure to chemical mixtures has been identified as a high priority in environment health science. Awareness of the potential effects of low-level chemical exposures is rising. The U.S. Geological Survey, in cooperation with the U.S. Environmental Protection Agency, conducted a study in which samples were collected from 38 streams in 25 States to provide an overview of contaminants found in stream water across the Nation. Additionally, biological screening assays were used to help determine any potential ecological and human-health effects of these chemical mixtures and to prioritize target chemicals for future toxicological studies. This report describes the site locations and the sampling and analytical methods and quality-assurance procedures used in the study.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171011","issn":"2331-1258","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency","usgsCitation":"Romanok, K.M., Reilly, T.J., Barber, Larry, Boone, Scott, Buxton, H.T., Foreman, W.T., Furlong, E.T., Hladik, Michelle, Iwanowicz, L.R., Journey, Celeste, Kolpin, D.W., Kuivila, Kathryn, Loftin, K.A., Mills, M.A., Meyer, M.T., Orlando, J.L., Smalling, K.L., Villeneuve, D.L., and Bradley, P.M., 2017, Methods used to characterize the chemical composition and biological activity of environmental waters throughout the United States, 2012–14: U.S. Geological Survey Open-File Report 2017–1011, 105 p., https://doi.org/10.3133/ofr20171011.","productDescription":"Report: x, 105 p.; 1 Table; Data release","numberOfPages":"120","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-080395","costCenters":[{"id":13634,"text":"South Atlantic Water Science 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\"}}]}","contact":"Director, South Atlantic Water Science Center
U.S. Geological Survey
720 Gracern Road
Stephenson Center, Suite 129
Columbia, SC 29210
https:www.usgs.gov/water/southatlantic/
The substantial numbers of golden eagles (Aquila chrysaetos) killed by collisions with oldgeneration wind turbines each year at the Altamont Pass Wind Resource Area (APWRA) in California has been well documented from previous studies. Few eagle nests have been documented in the APWRA, however, and adults and subadults 3+ years of age killed by turbines were generally not associated with nearby territories. We searched a subset of randomly selected survey plots for territorial pairs of golden eagles and associated nesting attempts within the APWRA as part of a broader investigation of population dynamics in the surrounding northern Diablo Range. In contrast to limited historical observations from 1988 to 2013, our surveys documented up to 15 territorial pairs within 3.2 kilometers (km) of wind turbines at the APWRA annually, 9 of which were not previously documented or only observed intermittently during historical surveys. We found evidence of nesting activity by adult pairs at least once during our study at six of these territories. We also determined that 23–36 percent of territories identified within 3.2 km of the APWRA had a subadult pair member, but that no pairs with a subadult member attempted to nest. These data will be useful to developers, wildlife managers, and future raptor studies in the area to evaluate and minimize the potential effects of wind energy or other development activities on previously unknown territorial pairs in the area.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171035","collaboration":"Prepared in cooperation with Next Era Energy, Inc., U.S. Fish and Wildlife Service, East Bay Regional Park District, and The Peregrine Fund, Inc.","usgsCitation":"Kolar, P.S., and Wiens, J.D., 2017, Distribution, nesting activities, and age-class of territorial pairs of golden\neagles at the Altamont Pass Wind Resource Area, California, 2014–16: U.S. Geological Survey Open-File\nReport 2017–1035, 18 p., https://doi.org/10.3133/ofr20171035.","productDescription":"iv, 18 p.","onlineOnly":"Y","ipdsId":"IP-081674","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":338092,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1035/ofr20171035.pdf","text":"Report","size":"1.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1035"},{"id":338091,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1035/coverthb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Altamont Pass Wind Resource Area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.8,\n 37.866667\n ],\n [\n -121.366667,\n 37.866667\n ],\n [\n -121.366667,\n 37.533333\n ],\n [\n -121.8,\n 37.533333\n ],\n [\n -121.8,\n 37.866667\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Forest and Rangeland Ecosystem Science Center
U.S. Geological Survey
777 NW 9th St., Suite 400
Corvallis, Oregon 97330
http://fresc.usgs.gov/
The age of the youngest volcanism at Eagle Lake, California, was investigated using stratigraphic, paleomagnetic, and 40Ar/39Ar techniques. The three youngest volcanic lava flows at Eagle Lake yielded ages of 130.0±5.1, 127.5±3.2 and 123.6±18.7 ka, and are statistically indistinguishable. Paleomagnetic results demonstrate that two of the lava flows are very closely spaced in time, whereas the third is different by centuries to at most a few millennia. These results indicate that the basalt lava flows at Eagle Lake are not Holocene in age, and were erupted during an episode of volcanism at about 130–125 ka that is unlikely to have spanned more than a few thousand years. Thus, the short-term potential for subsequent volcanism at Eagle Lake is considered low.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171027","usgsCitation":"Clynne, M.A., Calvert, A.T., Champion, D.E., Muffler, L.J.P., Sawlan, M.G., and Downs, D.T., 2017, Age of the youngest volcanism at Eagle Lake, northeastern California—40Ar/39Ar and paleomagnetic results: U.S. Geological Survey Open-File Report 2017–1027, 24 p., https://doi.org/10.3133/ofr20171027.","productDescription":"iii, 24 p.","onlineOnly":"Y","ipdsId":"IP-081567","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":338131,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1027/ofr20171027.pdf","text":"Report","size":"1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1027"},{"id":338130,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1027/coverthb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Eagle Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.95,\n 40.75\n ],\n [\n -120.625,\n 40.75\n ],\n [\n -120.625,\n 40.525\n ],\n [\n -120.95,\n 40.525\n ],\n [\n -120.95,\n 40.75\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Volcano Science Center—Menlo Park
U.S. Geological Survey
345 Middlefield Road, MS 910
Menlo Park, CA 94025
https://volcanoes.usgs.gov/
Abstract
Introduction
General Geology of the Eagle Lake Area
Previous Map Depiction of the Young Basalts at Eagle Lake
Unit Descriptions
Paleomagnetic Secular Variation
40Ar/39Ar Results
Discussion
Conclusions
Acknowledgments
References
Appendix 1. 40Ar/39Ar Analytical Techniques
Appendix 2. Tabulated 40Ar/39Ar data for basaltic flows of Eagle Lake
Between 1997 and 2011, Mongolia established three specially protected areas in the north-central part of the country to protect various high-value resources. These areas are jointly referred to as the Ulaan Taiga Specially Protected Areas. In accordance with the goals of the draft general management plan, this report identifies options for initiating an inventory and monitoring program for the three protected areas. Together, the three areas comprise over 1.5 million hectares of mountainous terrain west of Lake Hovsgol and bordering the Darkhad Valley. The area supports numerous rare ungulates, endangered fish, and over 40 species of threatened plants. Illegal mining, illegal logging, and poaching pose the most immediate threats to resources. As a first step, a review of published literature would inform natural resource management at the Ulaan Taiga Specially Protected Areas because it would inform other inventories.
Vegetation classification and mapping also would inform other inventory efforts because the process incorporates geographic analysis to identify environmental gradients, fine-scale sampling that captures species composition and structure, and landscape-scale results that represent the variety and extent of habitats for various organisms. Mapping using satellite imagery reduces the cost per hectare.
Following a determination of existing knowledge, field surveys of vertebrates and vascular plants would serve to build species lists and fill in gaps in existing knowledge. For abiotic resources, a focus on monitoring air quality, evaluating and monitoring water quality, and assembling and storing weather data would provide information for correlating resource response status with changing environmental conditions.
Finally, we identify datasets that, if incorporated into a geographic information system, would inform resource management. They include political boundaries, infrastructure, topography, surficial geology, hydrology, fire history, and soils.
In terms of tracking high-value resources, vegetation monitoring at the plot scale would provide a basis for detecting change in such characteristics as plant species composition, vegetation structure, and productivity that are associated with landscape-scale factors such as climate change or biotic interactions. Continued population monitoring of rare ungulates, particularly argali or wild sheep (Ovis ammon), would provide information on how populations are responding to natural and anthropogenic stressors. Siberian taimen (Hucho taimen) also is an important monitoring target given ongoing threats of poaching and climate change.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171025","usgsCitation":"Moore, P.E., Meyer, J.B., and Chow, L.S., 2017, Natural resource inventory and monitoring for Ulaan Taiga Specially Protected Areas—An assessment of needs and opportunities in northern Mongolia: U.S. Geological Survey Open-File Report 2017–1025, 35 p., https://doi.org/10.3133/ofr20171025.","productDescription":"viii, 35 p.","numberOfPages":"48","onlineOnly":"Y","ipdsId":"IP-082861","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":337345,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1025/coverthb.jpg"},{"id":337346,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1025/ofr20171025.pdf","text":"Report","size":"3.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1025"}],"country":"Mongolia","otherGeospatial":"Ulaan Taiga Specially Protected Areas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 97.55859375,\n 49.89463439573421\n ],\n [\n 102.48046875,\n 49.89463439573421\n ],\n [\n 102.48046875,\n 52.24125614966341\n ],\n [\n 97.55859375,\n 52.24125614966341\n ],\n [\n 97.55859375,\n 49.89463439573421\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
http://www.werc.usgs.gov/