Dry conditions during 2020 and 2021 affected the water supply within the Souris River Basin and highlighted the need for better understanding of the streamflow dynamics for managing the resource during low-flow conditions. In June 2021, a loss of streamflow was observed on the Souris River between U.S. Geological Survey streamgages on the Souris River near Foxholm, North Dakota (site 1), and near Verendrye, N. Dak. (site 22). The largest loss was upstream from the Souris River above Minot, N. Dak. (site 7). On June 6, 2021, the daily mean streamflow decreased from 33.8 cubic feet per second at site 1 to 16.3 cubic feet per second at site 7, a loss of 17.5 cubic feet per second. To better understand where streamflow losses occurred in the reach from site 1 to site 22, multiple sites were selected for streamflow measurements between the three streamgages (sites 1, 7, and 22). Streamflow measurements made at 22 selected sites on the Souris River on August 31 and September 1, 2021, did not indicate the loss in streamflow that was observed at the three streamgages (sites 1, 7, and 22) in June 2021. Measurements made at the three streamgages (sites 1, 7, and 22) on August 31 had streamflows of 44.2, 45.9, and 46.8 cubic feet per second, respectively. Streamflow measured at all 22 sites on August 31 and September 1 on the Souris River ranged from 38.4 (site 9) to 49.8 cubic feet per second (site 12). In general, the largest change in streamflow was measured among sites on the Souris River in or near the city of Minot, N. Dak.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221005","collaboration":"Prepared in cooperation with the North Dakota Department of Water Resources, the U.S. Fish and Wildlife Service, and the U.S. Army Corps of Engineers, St. Paul District","usgsCitation":"Galloway, J.M., and Hanson, B.R., 2022, Measurements of streamflow gain and loss on the Souris River between Lake Darling and Verendrye, North Dakota, August 31 and September 1, 2021: U.S. Geological Survey Open-File Report 2022–1005, 10 p., https://doi.org/10.3133/ofr20221005.","productDescription":"Report: vi, 10 p.; Dataset","numberOfPages":"20","onlineOnly":"Y","ipdsId":"IP-135605","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":394315,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1005/coverthb.jpg"},{"id":394317,"rank":3,"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":394316,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1005/ofr20221005.pdf","text":"Report","size":"1.23 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022–1005"},{"id":501538,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112123.htm","linkFileType":{"id":5,"text":"html"}},{"id":394329,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1005/images","description":"OFR 2022–1005 images"},{"id":394328,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1005/ofr20221005.XML","description":"OFR 2022–1005 XML"}],"country":"United States","state":"North Dakota","otherGeospatial":"Souris River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -101.6667,\n 48\n ],\n [\n -100.5,\n 48\n ],\n [\n -100.5,\n 48.50\n ],\n [\n -101.6667,\n 48.50\n ],\n [\n -101.6667,\n 48\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Dakota Water Science Center
U.S. Geological Survey
821 East Interstate Avenue
Bismarck, ND 58503
1608 Mountain View Road
Rapid City, SD 57702
This report addresses system characterization of Planet’s SkySat and is part of a series of system characterization reports produced and delivered by the U.S. Geological Survey Earth Resources Observation and Science Cal/Val Center of Excellence. These reports present and detail the methodology and procedures for characterization; present technical and operational information about the specific sensing system being evaluated; and provide a summary of test measurements, data retention practices, data analysis results, and conclusions.
SkySat is a constellation of submeter resolution Earth observation satellites providing analytics services, high-definition video, and imagery. The goal for the constellation is to capture multiple daily repeats of high-resolution imagery over any spot on the Earth. As of September 2020, 21 SkySat satellites have been launched, and the first launch occurred in November 2013. More information on Planet satellites and sensors is available in the “2020 Joint Agency Commercial Imagery Evaluation—Remote Sensing Satellite Compendium” and from the manufacturer at https://www.planet.com/.
The Earth Resources Observation and Science Cal/Val Center of Excellence system characterization team completed data analyses to characterize the geometric (interior and exterior), radiometric, and spatial performances. Results of these analyses indicate that SkySat has an interior geometric performance in the range of a 0.38- (0.47 pixel) to 0.75-meter (m; 0.93 pixel) root mean square error in easting and a 0.27- (0.33 pixel) to 0.55-m (0.68 pixel) root mean square error in northing, in band-to-band registration; an exterior geometric performance in the range of 0.26 (0.32 pixel) to 1.04 m (1.28 pixels) offset in comparison to ground control points; a radiometric performance in the range of 0.033 to 0.797 (linear regression); and a spatial performance in the range of 3.7 to 4.3 pixels at full width at half maximum, with a modulation transfer function at a Nyquist frequency in the range of 0.004 to 0.009.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"System characterization of Earth observation sensors","largerWorkSubtype":{"id":4,"text":"Other Government Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211030E","usgsCitation":"Kim, M., Park, S., Sampath, A., Anderson, C., and Stensaas, G.L., 2022, System characterization report on Planet SkySat, chap. E of Ramaseri Chandra, S.N., comp., System characterization of Earth observation sensors: U.S. Geological Survey Open-File Report 2021–1030, 17 p., https://doi.org/10.3133/ofr20211030E.","productDescription":"iv, 17 p.","numberOfPages":"17","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-126680","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":394021,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1030/e/coverthb.jpg"},{"id":394022,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1030/e/ofr20211030e.pdf","text":"Report","size":"1.73 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1030-E"}],"contact":"Director, Earth Resources Observation and Science (EROS) Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
The Des Moines River alluvial aquifer is an important source of water for Des Moines Water Works, the municipal water utility that provides residential and commercial water resources to the residents of Des Moines, Iowa, and surrounding municipalities. As an initial step in developing a better understanding of the groundwater resources of the Des Moines River alluvial aquifer, the U.S. Geological Survey constructed a steady-state numerical groundwater flow model in cooperation with Des Moines Water Works to simulate water-table elevations in the Des Moines River alluvial aquifer near Prospect Park in Des Moines under winter low-flow conditions.
A simple conceptual model consisting of a hydrogeologic framework, water budget, and inferred water-table elevation map was developed for the model area. The inferred water-table elevation map was constructed based on general knowledge of hydrogeology within the model area and was used to set calibration targets for numerical model calibration. A steady-state numerical model was constructed based on the conceptual model using MODFLOW-NWT to simulate an area of about 15 square kilometers near Prospect Park in Des Moines. Parameter ESTimation software was used for model calibration to assess and optimize performance of the horizontal hydraulic conductivity and recharge parameters. The numerical groundwater flow model and supporting data are available in the USGS data release associated with this report, which contains the model archive.
Performance of the calibrated steady-state model was assessed by comparing observed and simulated water-table elevations, as well as estimated and simulated contributions to streamflow within the model area. The difference between observed water-table elevations and simulated water-table elevations was −0.1 meter at the majority of calibration targets, with the negative value indicating an overestimation of the simulated water-table elevation value compared to the observed water-table elevation value, and the root mean square error was 0.13 meter, which represents about 20 percent of the difference in observed water-table elevations. The simulated value of contributions to streamflow within the model area was considered similar to the estimated value, increasing confidence in the ability of the model to accurately represent the groundwater flow system in the Des Moines River alluvial aquifer in the model area during winter low-flow conditions.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211110","collaboration":"Prepared in cooperation with Des Moines Water Works","usgsCitation":"FitzGerald, K.M., Ha, W.S., Haj, A.E., Gruhn, L.R., Bristow, E.L., and Weber, J.R., 2022, A steady-state groundwater flow model for the Des Moines River alluvial aquifer near Prospect Park, Des Moines, Iowa: U.S. Geological Survey Open-File Report 2021–1110, 20 p., https://doi.org/10.3133/ofr20211110.","productDescription":"Report: vii, 20 p.; Data Release; Dataset","numberOfPages":"20","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-130288","costCenters":[{"id":351,"text":"Iowa Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":501532,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112070.htm","linkFileType":{"id":5,"text":"html"}},{"id":393916,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1110/ofr20211110.pdf","text":"Report","size":"2.45 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1110"},{"id":393915,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1110/coverthb.jpg"},{"id":393917,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9F3CKLC","text":"USGS data release","linkHelpText":"MODFLOW-NWT model used to simulate groundwater levels in the Des Moines River alluvial aquifer near Des Moines, Iowa"},{"id":393918,"rank":4,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"- USGS water data for the Nation"}],"country":"United States","state":"Iowa","city":"Des Moines","otherGeospatial":"Prospect Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.65175247192383,\n 41.611463744813506\n ],\n [\n -93.61836433410645,\n 41.611463744813506\n ],\n [\n -93.61836433410645,\n 41.63019942878951\n ],\n [\n -93.65175247192383,\n 41.63019942878951\n ],\n [\n -93.65175247192383,\n 41.611463744813506\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
400 South Clinton Street, Suite 269
Iowa City, IA 52240
This report addresses system characterization of Satellogic’s NewSat satellite (also known as ÑuSat) and is part of a series of system characterization reports produced and delivered by the U.S. Geological Survey Earth Resources Observation and Science Cal/Val Center of Excellence. These reports present and detail the methodology and procedures for characterization; present technical and operational information about the specific sensing system being evaluated; and provide a summary of test measurements, data retention practices, data analysis results, and conclusions.
Since 2016, Satellogic has launched 17 NewSat satellites. All NewSat satellites have four-band imagers with a 1-meter (m) ground sample distance, and values in pixels are identical to values in meters. All NewSats have been launched into Sun-synchronous orbits of about 475 kilometers, with inclinations of about 97.5 degrees. The satellites have expected lifetimes of about 3 years. More information on the Satellogic satellites and sensors is available in the “2020 Joint Agency Commercial Imagery Evaluation—Remote Sensing Satellite Compendium” and from the manufacturer at https://satellogic.com/.
The Earth Resources Observation and Science Cal/Val Center of Excellence system characterization team completed data analyses to characterize the geometric (interior and exterior), radiometric, and spatial performances. Results of these analyses indicate that the NewSat satellites have an interior geometric performance in the range of −0.119 (−0.119 pixel) to 0.020 m (0.020 pixel) in easting and −0.148 (−0.148 pixel) to 0.014 m (0.014 pixel) in northing in band-to-band registration, an exterior geometric performance of −9.04 (−9.04 pixels) to −5.84 m (−5.84 pixels) in easting and 1.25 (1.25 pixels) to 3.11 m (3.11 pixels) in northing offset in comparison to Sentinel-2, an exterior geometric performance using ground control points of a 6.5-m circular error (95 percent), a radiometric performance in the range of 0.034 to 0.081 in offset and 0.652 to 0.808 in slope, and a spatial performance in the range of 1.61 to 1.76 pixels for full width at half maximum, with a modulation transfer function at a Nyquist frequency in the range of 0.081 to 0.138.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"System characterization of Earth observation sensors","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211030L","usgsCitation":"Vrabel, J.C., Bresnahan, P., Stensaas, G.L., Anderson, C., Christopherson, J., Kim, M., and Park, S., 2022, System characterization report on the Satellogic NewSat multispectral sensor (ver. 1.1, April 2022), chap. L of Ramaseri Chandra, S.N., comp., System characterization of Earth observation sensors: U.S. Geological Survey Open-File Report 2021–1030, 28 p., https://doi.org/10.3133/ofr20211030L.","productDescription":"v, 28 p.","numberOfPages":"28","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-135435","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":393738,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1030/l/ofr20211030l.pdf","text":"Report","size":"7.48 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1030-L"},{"id":393737,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1030/l/coverthb2.jpg"},{"id":399739,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2021/1030/l/versionHist.txt","size":"1 kB"}],"edition":"Version 1.0: January 3, 2022; Version 1.1: April 28, 2022","contact":"Director, Earth Resources Observation and Science (EROS) Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
We conducted the first assessment of eelgrass and seaweed distribution and abundance along the coast of the Alaska Peninsula-Becharof National Wildlife Refuges in Chignik Lagoon and Mud Bay. Areal extent of eelgrass, as determined from remote-sensing techniques, was estimated to be 2,414 hectares in Chignik Lagoon and 188 hectares in Mud Bay, and eelgrass was the dominant marine macrophyte in each of the embayments. During an embayment-wide point survey of Chignik Lagoon, eelgrass and seaweeds were observed on 76 and 62 percent of survey points, respectively. Average percent cover was greater for eelgrass (82 percent) than for seaweeds (37 percent) when each was present at a survey point. In contrast, eelgrass and seaweeds were distributed nearly equally in Mud Bay, occurring on 64 and 70 percent of the points, respectively, and when present, cover of eelgrass and seaweeds were 70 and 60 percent, respectively. Brown and red seaweeds, such as Polysiphonia pacifica, Saccharina latissima, Neorhodomela oregona, and Eudesme borealis, were the most common seaweeds in Chignik Lagoon, while green seaweeds, particularly Kornmannia leptoderma and Cladophora sericea, were dominant in Mud Bay. Standing crop of eelgrass was 44 percent greater in Chignik Lagoon (98.0±6.4 grams dry weight per square meter) than in Mud Bay (68.3±6.7 grams dry weight per square meter) in 2010. Five types of macro-invertebrates were assessed during the point survey. At least one of these macro-invertebrates was observed on 45 percent of points in Chignik Lagoon and 64 percent of points in Mud Bay. Gastropods were the most common of the macro-invertebrates, occurring on 40–57 percent of points in each of the embayments. This assessment of eelgrass and seaweeds can serve as a baseline for determining future changes in the distribution and abundance of these marine macrophytes in Chignik Lagoon and Mud Bay.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201144","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service","usgsCitation":"Ward, D.H., Hogrefe, K.R., Donnelly,T.F., Fairchild, L.L., and Britton, R., 2022, Eelgrass (Zostera marina) and seaweed assessment Alaska Peninsula-Becharof National Wildlife Refuges, 2010: U.S. Geological Survey Open-File Report 2020–1144, 14 p., https://doi.org/10.3133/ofr20201144.","productDescription":"Report v, 14 p.; 2 Data Releases","onlineOnly":"Y","ipdsId":"IP-118490","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":405810,"rank":9,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20211034","text":"OFR 2021-1034 —","description":"OFR 2021-1034","linkHelpText":"Inventory of eelgrass (Zostera marina) and seaweeds at the end of the Alaska Peninsula, August–September 2012"},{"id":405809,"rank":8,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20201143","text":"OFR 2020-1143 —","description":"OFR 2020-1143","linkHelpText":"Eelgrass (Zostera marina) and seaweed abundance along the coast of Nunivak Island, Yukon Delta National Wildlife Refuge, Alaska, 2010"},{"id":405808,"rank":7,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20201114","text":"OFR 2020-1114 —","description":"OFR 2020-1114","linkHelpText":"Eelgrass (Zostera marina) and Seaweed Abundance along the Coast of Togiak National Wildlife Refuge, Alaska, 2008–10"},{"id":384519,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1144/coverthb1.jpg"},{"id":384520,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1144/ofr20201144.pdf","text":"Report","size":"1.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1144"},{"id":384521,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9WEK4JI","text":"USGS data release","description":"USGS data release","linkHelpText":"Imagery and mapping data of eelgrass (Zostera marina) distribution, Alaska and Baja California, Mexico"},{"id":384522,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9URZJYW","text":"USGS data release","description":"USGS data release","linkHelpText":"Point sampling data for eelgrass (Zostera marina) and seaweed distribution and abundance in bays adjacent to the Alaska Peninsula-Becharof National Wildlife Refuges, Alaska, 2010"},{"id":405806,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20201035","text":"OFR 2020-1035 —","description":"OFR 2020-1035","linkHelpText":"Abundance and distribution of eelgrass (Zostera marina) and seaweeds at Izembek National Wildlife Refuge, Alaska, 2007–10"},{"id":405807,"rank":6,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20201080","text":"OFR 2020-1080 —","description":"OFR 2020-1080","linkHelpText":"Distribution of eelgrass (Zostera marina) in coastal waters adjacent to Togiak National Wildlife Refuge, Alaska"}],"country":"United States","state":"Alaska","otherGeospatial":"Alaska Peninsula-Becharof National Wildlife Refuges","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -158.75,\n 56.2333\n ],\n [\n -158.333,\n 56.2333\n ],\n [\n -158.333,\n 56.3667\n ],\n [\n -158.75,\n 56.3667\n ],\n [\n -158.75,\n 56.2333\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Alaska Science Center
U.S. Geological Survey
4210 University Drive
Anchorage, Alaska 99508
The Valmont TCE Superfund Site, Luzerne County, Pennsylvania is underlain by fractured and folded sandstones and shales of the Pottsville and Mauch Chunk Formations, which form a fractured-rock aquifer recharged locally by precipitation. Industrial activities at the former Chromatex Plant resulted in trichloroethene (TCE) contamination of groundwater at and near the facility, which was identified in 1987 and led to listing as a Superfund site by the U.S. Environmental Protection Agency (EPA) in 1989. To address the problem of TCE concentrations in nearby residential wells that exceed the maximum contaminant level (MCL) of 5 micrograms per liter (μg/L), alternate water supplies were provided. A 2015 review of initial characterization and subsequent remediation by the EPA identified the need for an updated understanding of the complex hydrogeology and the conceptual site model. Additional contaminants present in groundwater at the site include some other volatile organic compounds (VOCs) and per- and polyfluoroalkyl substances (PFAS), predominantly consisting of perfluorooctanoic acid (PFOA) and perfluorooctanesulfonic acid (PFOS) present in concentrations that exceeded the EPA Health Advisory (HA) level of 5 nanograms per liter (ng/L) for combined PFOA and PFOS.
In response to a request from the EPA in 2019, the U.S. Geological Survey (USGS) prepared cross sections and maps to provide more information about the hydrogeologic framework at and near the site and assist in improving the conceptual site model using water level and contaminant data collected by the EPA in 2020. The cross sections present lithologic correlations from available geophysical logs collected in wells from 2002 to 2014; they show alternating intervals of relatively elevated and reduced natural gamma activity that correspond to changes in lithology, with water-bearing zones and well screens commonly located at lithologic contacts, sometimes near thin coal seams. Water-bearing zones commonly are associated with fractures at or near lithologic contacts but also may be associated with fractures at or near apparent faulting. Recent (March 2020) water-level data shown on cross sections and maps indicate large downward vertical gradients and apparent radial gradients laterally to the northeast, northwest, and southwest that generally following topography. Recent (February to March 2020) data for TCE groundwater concentration shown on cross sections and maps indicate the highest TCE concentrations (greater than 3,000 μg/L and as much as 75,000 μg/L) and combined PFOA and PFOS concentrations (greater than 1,000 ng/L and up to at least 2,350 ng/L) are from shallow (less than 60 feet [ft] below land surface [bls]) and intermediate depth (60 to 100 ft bls) wells near the center of the former Chromatex Plant. TCE and PFAS (as combined PFOA and PFOS) contamination is present at greater depths, as much as 304 ft bls, as evidenced by samples collected from one well (a reconstructed former production well) near the plant, that contained concentrations of about 240 μg/L and 508 ng/L, respectively. The 2020 data also indicate that TCE and PFAS concentrations which exceed drinking-water MCL or HA levels are present in groundwater depths of less than 200 ft in an area that extends predominantly in a northeast direction from the former Chromatex Plant, and is apparently influenced by hydraulic gradients, lithology, and geologic structure.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211093","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency","usgsCitation":"Senior, L.A., Fiore, A.R., and Bird, P.H., 2021, Hydrogeologic framework, water levels, and selected contaminant concentrations at Valmont TCE Superfund Site, Luzerne County, Pennsylvania, 2020 (ver. 1.1, August 2022): U.S. Geological Survey Open-File Report 2021–1093, 80 p., https://doi.org/10.3133/ofr20211093.","productDescription":"Report: xii, 80 p.; 17 Plates: 17.00 x 11.00 inches or smaller","numberOfPages":"80","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-128502","costCenters":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true},{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"links":[{"id":501527,"rank":21,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_111788.htm","linkFileType":{"id":5,"text":"html"}},{"id":389684,"rank":17,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/of/2021/1093/ofr20211093_plate15.pdf","text":"Plate 15","size":"470 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Section C-Cʹ with generalized potentiometric surfaces and trichloroethene concentrations, Valmont TCE Superfund Site, Luzerne County, Pennsylvania, February-March 2020"},{"id":389683,"rank":16,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/of/2021/1093/ofr20211093_plate14.pdf","text":"Plate 14","size":"919 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Section Bd-Bdʹ detail with generalized potentiometric surfaces and trichloroethene concentrations, Valmont TCE Superfund Site, Luzerne County, Pennsylvania, February-March 2020"},{"id":389680,"rank":13,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/of/2021/1093/ofr20211093_plate11.pdf","text":"Plate 11","size":"587 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Section A-Aʹ with generalized potentiometric surfaces and trichloroethene concentrations, Valmont TCE Superfund Site, Luzerne County, Pennsylvania, February-March 2020"},{"id":389679,"rank":12,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/of/2021/1093/ofr20211093_plate10.pdf","text":"Plate 10","size":"229 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Section H-Hʹ with geophysical log correlations and trichloroethene concentrations, Valmont TCE Superfund Site, Luzerne County, Pennsylvania"},{"id":389678,"rank":11,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/of/2021/1093/ofr20211093_plate9.pdf","text":"Plate 9","size":"1.96 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Section G-Gʹ with geophysical log correlations (A) and generalized potentiometric surfaces and trichloroethene concentrations (B), Valmont TCE Superfund Site, Luzerne County, Pennsylvania, February-March 2020"},{"id":389676,"rank":9,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/of/2021/1093/ofr20211093_plate7.pdf","text":"Plate 7","size":"287 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Section E-Eʹ with geophysical log correlations, Valmont TCE Superfund Site, Luzerne County, Pennsylvania"},{"id":389675,"rank":8,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/of/2021/1093/ofr20211093_plate6.pdf","text":"Plate 6","size":"293 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Section D-Dʹ with geophysical log correlations, generalized potentiometric surfaces, and trichloroethene concentrations, Valmont TCE Superfund Site, Luzerne County, Pennsylvania, February-March 2020"},{"id":389674,"rank":7,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/of/2021/1093/ofr20211093_plate5.pdf","text":"Plate 5","size":"303 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Section C-Cʹ with geophysical log correlations, Valmont TCE Superfund Site, Luzerne County, Pennsylvania"},{"id":389673,"rank":6,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/of/2021/1093/ofr20211093_plate4.pdf","text":"Plate 4","size":"2.32 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Section Bd-Bdʹ detail with geophysical log correlations, Valmont TCE Superfund Site, Luzerne County, Pennsylvania"},{"id":404943,"rank":20,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2021/1093/versionHist.txt","size":"1.31 KB","linkFileType":{"id":2,"text":"txt"}},{"id":389686,"rank":19,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/of/2021/1093/ofr20211093_plate17.pdf","text":"Plate 17","size":"774 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Pennsylvania"},{"id":389671,"rank":4,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/of/2021/1093/ofr20211093_plate2.pdf","text":"Plate 2","size":"220 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Section Ad-Adʹ detail with geophysical log correlations, Valmont TCE Superfund Site, Luzerne County, Pennsylvania"},{"id":389670,"rank":3,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/of/2021/1093/ofr20211093_plate1.pdf","text":"Plate 1","size":"277 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Section A-Aʹ with geophysical log correlations, Valmont TCE Superfund Site, Luzerne County, Pennsylvania"},{"id":389668,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1093/ofr20211093.pdf","text":"Report","size":"26.0 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1093"},{"id":389667,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1093/coverthb3.jpg"},{"id":389677,"rank":10,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/of/2021/1093/ofr20211093_plate8.pdf","text":"Plate 8","size":"1.98 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Section F-Fʹ with geophysical log correlations, Valmont TCE Superfund Site, Luzerne County, Pennsylvania"},{"id":389681,"rank":14,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/of/2021/1093/ofr20211093_plate12.pdf","text":"Plate 12","size":"396 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Section Ad-Adʹ detail with generalized potentiometric surfaces and trichloroethene concentrations, Valmont TCE Superfund Site, Luzerne County, Pennsylvania, February-March 2020"},{"id":389682,"rank":15,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/of/2021/1093/ofr20211093_plate13.pdf","text":"Plate 13","size":"0.99 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Section B-Bʹ with generalized potentiometric surfaces and trichloroethene concentrations, Valmont TCE Superfund Site, Luzerne County, Pennsylvania, February-March 2020"}],"country":"United States","state":"Pennsylvania","county":"Luzerne 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1.0: September 30, 2021; Version 1.1: August 9, 2022","contact":"Director, Pennsylvania Water Science Center
U.S. Geological Survey
215 Limekiln Road
New Cumberland, PA 17070-2424
A portable suction-dredge and sluice-box system were used to collect larval lampreys (Entosphenus tridentatus and Lampetra spp.) from fine and coarse sediment in field and laboratory tests. We evaluated the injury rate, survival, and burrowing capability of lamprey following passage through the dredge system and used collection of lamprey from water without sediment as a control. The system used a hydraulic eductor (also known as a Venturi valve) to create suction so that sediment and lamprey avoided passage through the pump impeller. For the field test, lamprey were tagged with visible elastomer implants based on small (89 millimeter [mm] or less) and large (92 mm or more) size categories and stocked into mesh enclosures over fine or coarse sediment. The dredge was used inside each enclosure to collect lamprey and they were transported to the laboratory for evaluation and holding. The mean time to burrow was recorded for each study group (3 fine, 3 coarse, 3 controls) on the day of the field test; injury was evaluated at 24 hours; and survival was evaluated at 24 hours, and at 7 and 14 days after the test. The suction dredge collected 32 lamprey in fine sediment, 21 lamprey in coarse sediment, and 28 lamprey in the control group, including 30 lamprey that were not initially stocked. One lamprey died the day of the test (fine sediment) and 24 hours later, three lamprey were found to be injured (2 in fine and 1 in coarse sediment). No injuries or mortalities occurred in the control group. Lamprey burrowing performance was similar across the two treatment groups and the controls. The mean time for all fish in a group to burrow was highly variable. For all groups in a treatment combined, the mean burrow times were fastest for the fine treatment (9.8 minutes), followed by the controls (11.4 minutes) and the coarse treatment (11.6 minutes). The mean times to burrow for the main group of fish in each treatment group (those that burrowed in quick succession) were similar: 4.3 minutes for the fine group, 4.4 minutes for the coarse group, and 4.5 minutes for the controls. The laboratory test collected 147 lamprey (73 small and 74 large size category) from coarse sediment using the same procedures as the field test. One fish (small) was killed the day of the test, and six lamprey (3 small and 3 large) were found with injuries during the 24-hour exams. No mortalities were recorded 7 days after the test, when monitoring was terminated. The overall injury rate for the laboratory test was 4.1 percent and the mortality rate was 0.7 percent. Injuries in the field and laboratory tests were localized minor hemorrhages or red, irritated areas. The suction- dredge system appears to be a safe option to collect larval lamprey from sediment and will be a useful addition to lamprey assessment and salvage tools.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211116","collaboration":"Prepared in cooperation with U.S. Fish and Wildlife Service","usgsCitation":"Liedtke, T.L., Skalicky, J.J., and Weiland, L.K., 2022, Collection of larval lampreys (Entosphenus tridentatus and Lampetra spp.) using a portable suction dredge—A pilot test: U.S. Geological Survey Open-File Report 2021–1116, 12 p., https://doi.org/10.3133/ofr20211116.","productDescription":"vi, 12 p.","onlineOnly":"Y","ipdsId":"IP-129003","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":436076,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9B337X6","text":"USGS data release","linkHelpText":"Evaluating injury and mortality to larval lamprey collected out of sediment using a portable suction dredge"},{"id":394472,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1116/coverthb2.jpg"},{"id":394473,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1116/ofr20212116.pdf","text":"Report","size":"2.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1116"}],"country":"United States","state":"Washington","otherGeospatial":"Wind River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.8229293823242,\n 45.68651588881847\n ],\n [\n -121.74190521240234,\n 45.68651588881847\n ],\n [\n -121.74190521240234,\n 45.74380820334429\n ],\n [\n -121.8229293823242,\n 45.74380820334429\n ],\n [\n -121.8229293823242,\n 45.68651588881847\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115-5016
The High Point paleosol is 2.28-meters-thick aggradational soil developed in fining upward estuarine-alluvial sand and loess. The paleosol is exposed in a few shoreline cliff faces of Mason Neck, Virginia. Although a former A horizon is missing, the E, Bw, Bt, and C horizon sequence seen in the sediments indicates subaerial pedogenesis. Pedogenesis began with initial estuarine-alluvial floodplain emergence as sea level was lowering in late marine isotope stage 5 (MIS5) and MIS4, continued during eolian silt deposition accompanied by incorporation of the silt into the estuarine-alluvial sand, and ended with a period of loess and eolian sand deposition, erosion, and development of periglacial(?) features. Six optically stimulated luminescence ages provide an age range from 86 to 56 ka (thousand years ago) for sedimentary units below and above the paleosol. These ages indicate a 10,000- to 30,000-year interval in late MIS5 and MIS4 for these events to have occurred.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211113","usgsCitation":"Markewich, H.W., Wysocki, D.A., Pavich, M.J., Smoot, J.P., and Litwin, R.J., 2021, Stratigraphy and age of a prominent paleosol in a late Pleistocene sedimentary sequence, Mason Neck, Virginia: U.S. Geological Survey Open-File Report 2021–1113, 24 p., https://doi.org/10.3133/ofr20211113.","productDescription":"vii, 24 p.","numberOfPages":"36","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-129382","costCenters":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":393454,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1113/ofr20211113.pdf","text":"Report","size":"2.46 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1113"},{"id":393453,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1113/coverthb.jpg"}],"country":"United States","state":"Virginia","otherGeospatial":"Mason Neck","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -77.40966796875,\n 38.507340712903456\n ],\n [\n -76.7724609375,\n 38.507340712903456\n ],\n [\n -76.7724609375,\n 38.884619201291905\n ],\n [\n -77.40966796875,\n 38.884619201291905\n ],\n [\n -77.40966796875,\n 38.507340712903456\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Florence Bascom Geoscience Center
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 21092
Two existing flood management structures in U.S. Army Garrison Fort Gordon, Georgia, were evaluated for potential retrofitting to address water-quality impacts, pursuant of U.S. Army Garrison Fort Gordon’s storm water management program. Stormwater calculations were computed according to the Georgia Stormwater Management Manual, including drainage area delineations, design-storm runoff volumes and peak discharges, stage-storage and stage-discharge curves, and outflow calculations. The results of these analyses were compared to Georgia’s regulatory requirements for dry detention basins. The two existing flood management structures did not meet the requirements for a dry detention basin. Planning-level analyses for these basins indicate that the existing structures do not have adequate storage capacity for the overbank flood design-storm runoff volume (25-year, 24-hour storm) or the extreme flood design-storm runoff volume (100-year, 24-hour storm) and neither storm water structural control 2 nor storm water structural control 3 has the emergency spillway needed to safely convey overflows. Furthermore, land use changes (forest removal) and the risk for additional sediment loads to these structures may reduce available storage volume, increasing the risk for design failure. Three potential retrofit alternatives were provided for planning purposes only, with a brief discussion of advantages and disadvantages of each alternative retrofit strategy.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211121","collaboration":"Prepared in cooperation with the Environmental and Natural Resources Management Division of the U.S. Army Garrison Fort Gordon","usgsCitation":"Stillwell, C.C., 2021, Evaluation of two existing flood management structures in U.S. Army Garrison Fort Gordon, Georgia, 2020: U.S. Geological Survey Open-File Report 2021–1121, 16 p., https://doi.org/10.3133/ofr20211121.","productDescription":"v, 16 p.","numberOfPages":"16","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-126439","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":394595,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20211121/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":393446,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2021/1121/images/"},{"id":393440,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2021/1121/ofr20211121.XML"},{"id":393438,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1121/ofr20211121.pdf","text":"Report","size":"6.04 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1121"},{"id":393439,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1121/coverthb.jpg"}],"country":"United States","state":"Georgia","otherGeospatial":"Fort Gordon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -82.18254089355469,\n 33.415018689904805\n ],\n [\n -82.1664047241211,\n 33.415018689904805\n ],\n [\n -82.1664047241211,\n 33.42742998368805\n ],\n [\n -82.18254089355469,\n 33.42742998368805\n ],\n [\n -82.18254089355469,\n 33.415018689904805\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, South Atlantic Water Science Center
U.S. Geological Survey
1770 Corporate Drive Suite 500
Norcross, GA 30093
The U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Calibration and Validation (Cal/Val) Center of Excellence (ECCOE) focuses on improving the accuracy, precision, calibration, and product quality of remote-sensing data, leveraging years of multiscale optical system geometric and radiometric calibration and characterization experience. The ECCOE Landsat Cal/Val Team continually monitors the geometric and radiometric performance of active Landsat missions and makes calibration adjustments, as needed, to maintain data quality at the highest level.
This report provides observed geometric and radiometric analysis results for Landsats 7–8 for quarter 2 (April–June), 2021. All data used to compile the Cal/Val analysis results presented in this report are freely available from the USGS EarthExplorer website: https://earthexplorer.usgs.gov.
One specific activity that the Cal/Val Team continued to closely monitor this quarter was the Landsat 8 Thermal Infrared Sensor (TIRS) response degradation, which has been observed since the two November 2020 safehold events. Detailed analysis results characterizing this degradation have been included in this report. Additional information about the safehold events is here: https://www.usgs.gov/core-science-systems/nli/landsat/november-19-2020-landsat-8-data-availability-update-recent-safehold.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211105","usgsCitation":"Micijevic, E., Rengarajan, R., Haque, M.O., Lubke, M., Tuli, F.T., Shaw, J.L., Hasan, N., Denevan, A., Franks, S., Choate, M.J., Anderson, C., Markham, B., Thome, K., Kaita, E., Barsi, J., Levy, R., and Ong, L., 2021, ECCOE Landsat quarterly Calibration and Validation report — Quarter 2, 2021: U.S. Geological Survey Open-File Report 2021–1105, 40 p., https://doi.org/10.3133/ofr20211105.","productDescription":"vii, 40 p.","numberOfPages":"52","onlineOnly":"Y","ipdsId":"IP-130990","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":393445,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2021/1105/images"},{"id":393443,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1105/ofr20211105.pdf","text":"Report","size":"4.86 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021–1105"},{"id":393442,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1105/coverthb.jpg"},{"id":393444,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2021/1105/ofr20211105.XML","size":"118 kB","description":"OFR 2021–1105 xml"}],"contact":"Director, Earth Resources Observation and Science Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
Structured decision making is a systematic, transparent process for improving the quality of complex decisions by identifying measurable management objectives and feasible management actions; predicting the potential consequences of management actions relative to the stated objectives; and selecting a course of action that maximizes the total benefit achieved and balances tradeoffs among objectives. The U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, applied an existing, regional framework for structured decision making to develop a prototype tool for optimizing tidal marsh management decisions at the Eastern Shore of Virginia and Fisherman Island National Wildlife Refuges in Virginia. Refuge biologists, refuge managers, and research scientists identified multiple potential management actions to improve the ecological integrity of six marsh management units within the refuges, totaling about 575 hectares, and estimated the outcomes of each action in terms of performance metrics associated with each management objective. Value functions previously developed at the regional level were used to transform metric scores to a common utility scale, and utilities were summed to produce a single score representing the total management benefit that could be accrued from each potential management action. Constrained optimization was used to identify the set of management actions, one per marsh management unit, that could maximize total management benefits at different cost constraints at the refuge scale. Results indicated that, for the objectives and actions considered here, total management benefits may increase consistently up to approximately $143,000, but that further expenditures may yield diminishing return on investment. Potential management actions in optimal portfolios at total costs less than $143,000 included digging runnels by hand to improve drainage from the marsh surface, breaching a road to restore natural hydrology, trapping predators to enhance nest success of tidal marsh birds, and reducing the abundance of Odocoileus virginianus (white-tailed deer) to minimize their effects on marsh vegetation. The potential management benefits were derived from expected increases in number of tidal marsh obligate breeding birds, species richness of nekton, and density of spiders (as an indicator of trophic health); and an expected decrease in duration of surface flooding. The prototype presented here does not resolve management decisions; rather, it provides a framework for decision making at the Eastern Shore of Virginia and Fisherman Island National Wildlife Refuges that can be updated as new data and information become available. Insights from this process may also be useful to inform future habitat management planning at the refuges.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211117","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service","usgsCitation":"Neckles, H.A., Lyons, J.E., Nagel, J.L., Adamowicz, S.C., Mikula, T., Denmon, P., and Leffel, R., 2021, Optimization of salt marsh management at the Eastern Shore of Virginia and Fisherman Island National Wildlife Refuges, Virginia, through use of structured decision making: U.S. Geological Survey Open-File Report 2021–1117, 32 p., https://doi.org/10.3133/ofr20211117.","productDescription":"Report: vi, 32 p.; Database","numberOfPages":"32","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-131973","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":393431,"rank":5,"type":{"id":9,"text":"Database"},"url":"https://ecos.fws.gov/ServCat/Reference/Profile/121918","text":"U.S. Fish and Wildlife Service database","linkHelpText":"- Salt marsh integrity and Hurricane Sandy vegetation, bird and nekton data"},{"id":393427,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1117/coverthb.jpg"},{"id":393428,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1117/ofr20211117.pdf","text":"Report","size":"2.75 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1117"},{"id":393429,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2021/1117/images/"},{"id":393430,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2021/1117/ofr20211117.XML"}],"country":"United States","state":"Virginia","otherGeospatial":"Fisherman Island National Wildlife Refuge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.99586486816406,\n 37.072162624715375\n ],\n [\n -75.92857360839844,\n 37.072162624715375\n ],\n [\n -75.92857360839844,\n 37.14061402065652\n ],\n [\n -75.99586486816406,\n 37.14061402065652\n ],\n [\n -75.99586486816406,\n 37.072162624715375\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Eastern Ecological Science Center
U.S. Geological Survey
11649 Leetown Road
Kearneysville, WV 25430
Structured decision making is a systematic, transparent process for improving the quality of complex decisions by identifying measurable management objectives and feasible management actions; predicting the potential consequences of management actions relative to the stated objectives; and selecting a course of action that maximizes the total benefit achieved and balances tradeoffs among objectives. The U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, applied an existing, regional framework for structured decision making to develop a prototype tool for optimizing tidal marsh management decisions at the Moosehorn National Wildlife Refuge in Maine. Refuge biologists, refuge managers, and research scientists identified multiple potential management actions to improve the ecological integrity of four marsh management units within the refuge, totaling about 13 hectares, and estimated the outcomes of each action in terms of performance metrics associated with each management objective. Value functions previously developed at the regional level were used to transform metric scores to a common utility scale, and utilities were summed to produce a single score representing the total management benefit that could be accrued from each potential management action. Constrained optimization was used to identify the set of management actions, one per marsh management unit, that could maximize total management benefits at different cost constraints at the refuge scale. Results indicated that, for the objectives and actions considered here, total management benefits may increase consistently up to $1,000, and may continue to increase at a lower rate with further expenditures. Potential management actions in optimal portfolios at total costs less than or equal to $1,000 included improving nesting habitat for Ammodramus nelsoni (Nelson’s sparrow) or restoring hydrologic connections to the upper marsh in one marsh management unit (Hobart Stream West). The potential management benefits were derived from expected increases in the density of nekton and of spiders (as an indicator of trophic health). The prototype presented here does not resolve management decisions; rather, it provides a framework for decision making at the Moosehorn National Wildlife Refuge that can be updated for implementation as new data and information become available. Insights from this process may also be useful to inform future habitat management planning at the refuge.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211115","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service","usgsCitation":"Neckles, H.A., Lyons, J.E., Nagel, J.L., Adamowicz, S.C., Mikula, T., Mills, M., Brown, R.E., and Ramos, K., 2021, Optimization of salt marsh management at the Moosehorn National Wildlife Refuge, Maine, through use of structured decision making: U.S. Geological Survey Open-File Report 2021–1115, 28 p., https://doi.org/10.3133/ofr20211115.","productDescription":"Report: vi, 28 p.; Database","numberOfPages":"28","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-131976","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":393375,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1115/coverthb.jpg"},{"id":393376,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1115/ofr20211115.pdf","text":"Report","size":"4.5 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":393377,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2021/1115/ofr20211115.xml"},{"id":393379,"rank":5,"type":{"id":9,"text":"Database"},"url":"https://ecos.fws.gov/ServCat/Reference/Profile/121918","text":"U.S. Fish and Wildlife Service database","linkHelpText":"- Salt marsh integrity and Hurricane Sandy vegetation, bird and nekton data"},{"id":393378,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2021/1115/images"}],"country":"United States","state":"Maine","otherGeospatial":"Moosehorn National Wildlife Refuge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -67.27890014648438,\n 44.80132682904856\n ],\n [\n -67.15,\n 44.80132682904856\n ],\n [\n -67.15,\n 44.918625522424925\n ],\n [\n -67.27890014648438,\n 44.918625522424925\n ],\n [\n -67.27890014648438,\n 44.80132682904856\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Eastern Ecological Science Center
U.S. Geological Survey
11649 Leetown Road
Kearneysville, WV 25430
The August 14, 2021, magnitude 7.2 Nippes, Haiti, earthquake triggered thousands of landslides on the Tiburon Peninsula. The landslides directly caused fatalities and damage and impeded response efforts by blocking roads and causing other infrastructure damage. Adverse effects of the landslides likely will continue for months to years. This report presents an assessment of potential postearthquake landslide-related geologic hazards for the Tiburon Peninsula and a preliminary map of the landslides triggered by the earthquake. This hazard assessment is based on an emergency analysis of the currently available, postearthquake satellite imagery. In this report, we highlight specific areas of concern that may benefit from more detailed assessment and longer-term monitoring. Our mapping efforts revealed that at least 4,893 landslides were triggered across the Tiburon Peninsula by the earthquake and subsequent rainfall from Tropical Cyclone Grace. We also observed hundreds of landslide deposits potentially restricting flow in rivers and streams. In addition, we observed landslides that likely affected roads by rendering them impassable or susceptible to subsequent damage from existing landslides. Because of the preliminary nature of this report and the limits of remote analyses, additional investigation and monitoring would be beneficial to accurately determine the threat posed by these hazards to people and infrastructure.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211112","usgsCitation":"Martinez, S.N., Allstadt, K.E., Slaughter, S.L., Schmitt, R., Collins, E., Schaefer, L.N., and Ellison, S., 2021, Landslides triggered by the August 14, 2021, magnitude 7.2 Nippes, Haiti, earthquake: U.S. Geological Survey Open-File Report 2021–1112, 17 p., https://doi.org/10.3133/ofr20211112.","productDescription":"Report; vi, 17 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-134308","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":393322,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P99MYPXK","text":"USGS data release","linkHelpText":"Rapid Response Landslide Inventory for the 14 August 2021 M7.2 Nippes, Haiti, Earthquake"},{"id":393237,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1112/ofr20211112.pdf","text":"Report","size":"19.0 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1112"},{"id":393236,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1112/coverthb.jpg"}],"country":"Haiti","otherGeospatial":"Nippes","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.50927734375,\n 17.95260646769184\n ],\n [\n -73.36669921875,\n 17.95260646769184\n ],\n [\n -73.36669921875,\n 18.729501999072138\n ],\n [\n -74.50927734375,\n 18.729501999072138\n ],\n [\n -74.50927734375,\n 17.95260646769184\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Geologic Hazards Science Center
U.S. Geological Survey
Box 25046, MS 966
Denver, CO 80225
Populations of federally endangered Lost River (Deltistes luxatus) and shortnose suckers (Chasmistes brevirostris) in Upper Klamath Lake, Oregon, and Clear Lake Reservoir (hereinafter, Clear Lake), California, are experiencing long-term decreases in abundance. Upper Klamath Lake populations are decreasing not only due to adult mortality, which is relatively low, but also because they are not being balanced by recruitment of young adult suckers into known adult spawning aggregations.
Long-term monitoring of juvenile sucker populations is conducted to (1) determine if there are annual and species-specific differences in production, survival, and growth, (2) better understand when juvenile sucker mortality is greatest, and (3) help identify potential causes of high juvenile sucker mortality particularly in Upper Klamath Lake. The U.S. Geological Survey monitoring program, that began in 2015, tracks cohorts through summer months and among years in Upper Klamath and Clear Lakes. Data on juvenile suckers captured in trap nets are used to provide information on annual variability in age-0 sucker apparent production, juvenile sucker apparent survival, apparent growth, species composition, and health.
Upper Klamath Lake indices of year-class strength indicated that the 2019 year-class was the strongest in the past 5 years of monitoring. Low detections of age-1 and older suckers indicate that the 2018 cohort experienced poor survival within the first year of life. Shortnose suckers constituted the smallest proportion and suckers with uncertain species identification constituted the largest proportion of the 2019 year-class. Small numbers of Lost River sucker were captured consistently throughout the sampling season.
The relative abundance of age-0 suckers is not a good indicator of year-class strength in Clear Lake. There were no age-0 suckers captured in Clear Lake during the 2015 and 2019 sampling seasons. Most suckers captured were age-1 Klamath largescale/shortnose suckers, which indicated a relatively strong 2018 cohort. Four-year old juveniles from the 2015 cohort were present in 2019 in Clear Lake. Cohorts that do not recruit to our sampling gear until a year or more of age seem to indicate that (1) a stream resident life history is contributing to the lake population and (2) juvenile suckers occupy the Willow Creek drainage for a full year or more. Although these suckers could be either the non-endangered Klamath largescale or the endangered shortnose suckers, a stream resident life history is consistent with these fish being Klamath largescale suckers. Survival of all distinguishable taxa of juvenile suckers is much higher in Clear Lake than in Upper Klamath Lake, with non-trivial numbers of suckers surviving to join spawning aggregations in most years.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211119","collaboration":"Prepared in cooperation with the Bureau of Reclamation","usgsCitation":"Bart, R.J., Kelsey, C.M., Burdick, S.M., Hoy, M.S., and Ostberg, C.O., 2021, Growth, survival, and cohort formation of juvenile Lost River (Deltistes luxatus) and shortnose suckers (Chasmistes brevirostris) in Upper Klamath Lake, Oregon, and Clear Lake Reservoir, California—2019 Monitoring Report: U.S. Geological Survey Open-File Report 2021–1119, 26 p., https://doi.org/10.3133/ofr20211119.","productDescription":"vi, 26 p.","onlineOnly":"Y","ipdsId":"IP-125594","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":393120,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1119/ofr20211119.pdf","text":"Report","size":"3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1119"},{"id":393119,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1119/coverthb.jpg"}],"country":"United States","state":"California, Oregon","otherGeospatial":"Clear Lake Reservoir, Upper Klamath Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.16384887695312,\n 42.21733067375916\n ],\n [\n -121.75048828124999,\n 42.21733067375916\n ],\n [\n -121.75048828124999,\n 42.595554553719204\n ],\n [\n -122.16384887695312,\n 42.595554553719204\n ],\n [\n -122.16384887695312,\n 42.21733067375916\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.25473022460936,\n 41.78052894057897\n ],\n [\n -121.04324340820312,\n 41.78052894057897\n ],\n [\n -121.04324340820312,\n 41.94621306986162\n ],\n [\n -121.25473022460936,\n 41.94621306986162\n ],\n [\n -121.25473022460936,\n 41.78052894057897\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115-5016
Flow management is complex in the Willamette River Basin where the U.S. Army Corps of Engineers owns and operates a system of 13 dams and reservoirs (hereinafter Willamette Project), which are spread throughout three large tributaries including the Middle Fork Willamette, McKenzie, and Santiam Rivers. The primary purpose of the Willamette Project is flood-risk management, which provides critical protection to the Willamette Valley, but flow managers must also consider factors such as power generation, water-quality improvement, irrigation, recreation, and protection for aquatic species such as U.S. Endangered Species Act-listed Chinook salmon (Oncorhynchus tshawytscha) and steelhead (O. mykiss). Flow-management decision-making in the basin can benefit from models that allow for flow-scenario comparisons and a wide range of modeling methods are available. For this study, we examined existing datasets and modeling efforts in the basin and provided an overview of available options. Most previous studies used Physical Habitat Simulation System, habitat data were collected from a series of transects within modeled reaches, and habitat suitability indices were obtained from the literature, or using expert opinion. These studies provide information for specific reaches of the Willamette River Basin, which limits their ability to provide broad-scale predictive capability. Recent efforts to develop a two-dimensional hydraulic model in the mainstem Willamette River, and in specific reaches of primary tributaries downstream from Project dams, have bolstered modeling capabilities in the basin. This work has developed spatially continuous water depth and velocity data in more than 250 kilometers (km) of river downstream from Project dams and has predictive capability throughout the year at flows up to normal peak levels. Additionally, other methods are described for estimating habitat availability, which include habitat suitability criteria, logistic regression, occupancy and abundance modeling, and energetic based approaches. There are strengths and weaknesses to each approach and selection of the preferred approach in the Willamette River Basin will depend on the desired metrics of interest and the risk tolerance of managers and stakeholders in the basin.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211114","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers","usgsCitation":"Kock, T.J., Perry, R.W., Hansen, G.S., White, J., Stratton Garvin, L., and Wallick, J.R., 2021, Synthesis of habitat availability and carrying capacity research to support water management decisions and enhance conditions for Pacific salmon in the Willamette River, Oregon: U.S. Geological Survey Open-File Report 2021–1114, 24 p., https://doi.org/10.3133/ofr20211114.","productDescription":"vii, 24 p.","onlineOnly":"Y","ipdsId":"IP-127909","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true},{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":393073,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1114/ofr20211114.pdf","text":"Report","size":"20 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1114"},{"id":393072,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1114/coverthb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Willamette River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.33251953125,\n 43.41302868475145\n ],\n [\n -121.59667968749999,\n 43.41302868475145\n ],\n [\n -121.59667968749999,\n 45.79050946752472\n ],\n [\n -123.33251953125,\n 45.79050946752472\n ],\n [\n -123.33251953125,\n 43.41302868475145\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115-5016
Golden Eagles (Aquila chrysaetos) are among the highest profile species killed by collisions with wind turbines at the Altamont Pass Wind Resource Area (APWRA) in the northern Diablo Range of west-central California. Understanding the distribution, site occupancy, and nesting status of eagles near the APWRA is needed to identify and minimize possible population-level impacts. We established a broad-scale survey design across a 5,185-km square-kilometer region of the Diablo Range, including the APWRA, to monitor site occupancy, abundance, and reproduction of Golden Eagles. During the study period we identified as many as 230 territorial pairs of Golden Eagles in the study area, up to 21 of which overlapped with the APWRA in any given year. On average, we detected a similar density of pairs at sites surveyed in the APWRA (1 pair per 17.8 km2 surveyed) relative to sites surveyed in the surrounding region (1 pair per 20.3 km2 surveyed). In 2020 and 2021, estimates of the proportion of pairs that successfully fledged at least one young (nesting success) were 0.27 and 0.26, respectively, which were above the seven-year average (0.22). On average, eagle pairs monitored in the APWRA had similar reproductive output (0.37 young fledged per pair) relative to pairs monitored outside of the APWRA (0.30 young fledged per pair). We observed a substantially higher proportion of territorial subadult pair members at the APWRA (mean = 29 percent of 16 pairs aged) relative to pairs monitored and aged in the surrounding region (mean = 3 percent of 122 pairs), indicating potentially higher rates of adult mortality or displacement at territories overlapping with the APWRA. Emergent threats to eagles in the study region, including a severe wildfire that impacted over 60 historical breeding territories in 2020, may interact with existing threats to affect population status and thus warrants further investigation. Our study provides wind energy developers, land managers, and regulatory agencies with key information on the spatial distribution, habitat quality, and population status of Golden Eagles needed to promote compatible wind energy production and long-term conservation of this federally protected wildlife species.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211107","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service and East Bay Regional Park District","usgsCitation":"Wiens, J.D., and Kolar, P.S., 2021, Golden eagle population surveys in the vicinity of the Altamont Pass Wind Resource Area, California, 2014–21: U.S. Geological Survey Open-File Report 2021–1107, 18 p., https://doi.org/10.3133/ofr20211107.","productDescription":"iv, 17 p.","onlineOnly":"Y","ipdsId":"IP-124051","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":402981,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20211107/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2021-1107"},{"id":397094,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2021/1107/images"},{"id":397095,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2021/1107/ofr20211107.XML"},{"id":393064,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1107/coverthb.jpg"},{"id":393065,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1107/ofr20211107.pdf","text":"Report","size":"9.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1107"}],"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 -121.94000244140624,\n 37.40943717748788\n ],\n [\n -121.30004882812499,\n 37.40943717748788\n ],\n [\n -121.30004882812499,\n 37.86834903305901\n ],\n [\n -121.94000244140624,\n 37.86834903305901\n ],\n [\n -121.94000244140624,\n 37.40943717748788\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
Pre-Cretaceous, predominantly dioritic plutonic rocks in the Lane Mountain area, California, intrude metasedimentary and metavolcanic rocks considered part of the El Paso terrane. New geochronologic (uranium-lead zircon), geochemical, and isotopic data provide a reliable basis for dividing these pre-Cretaceous plutonic rocks into two mappable suites of Permian–Triassic and Late Jurassic ages. The 26 Permian–Triassic samples included in this report have a mean age of ~248 mega-annum (Ma), range in composition from monzodiorite to quartz monzonite and granodiorite, and have a mean initial 87Sr/86Sr ratio (Sri) of ~0.7045. The 22 Late Jurassic samples have a mean age of ~149 Ma, range in composition from gabbro to granite, and have a mean Sri of ~0.7055. Accurate mapping of these two plutonic suites and their detailed field relations with the associated metamorphic rocks is essential for resolving the geologic history and regional tectonic significance of the Lane Mountain area.
The sub-0.706 Sri values of both plutonic suites at Lane Mountain are consistent with previous suggestions that the El Paso terrane is allochthonous and did not develop on Precambrian continental lithosphere. Both suites are considered parts of northwest-trending magmatic arcs interpreted to have formed above east-dipping subduction zones along the evolving North American continental margin, and both arcs are interpreted to cross a major east-west-trending boundary between the El Paso terrane and rocks considered part of ancestral North America in the San Bernardino Mountains area to the south. The El Paso terrane thus appears to have been attached to the San Bernardino Mountains area at least since Permian–Triassic time, although the boundary probably has been modified by Cenozoic faulting.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211094","usgsCitation":"Stone, P., Brown, H.J., Cecil, M.R., Fleck, R.J., Vazquez, J.A., and Fitzpatrick, J.A., 2021, Geochronologic, isotopic, and geochemical data from pre-Cretaceous plutonic rocks in the Lane Mountain area, San Bernardino County, California: U.S. Geological Survey Open-File Report 2021–1094, 74 p., https://doi.org/10.3133/ofr20211094.","productDescription":"viii, 74 p.","numberOfPages":"74","onlineOnly":"Y","ipdsId":"IP-121822","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":436088,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9KRD45S","text":"USGS data release","linkHelpText":"Tabular geochronologic, geochemical, and isotopic data from igneous rocks in the Lane Mountain area, San Bernardino County, 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Geology, Minerals, Energy, & Geophysics Science Center
Menlo Park, California
U.S. Geological Survey
Building 19, 350 N. Akron Rd.
P.O. Box 158
Moffett Field, CA 94035
The U.S. Geological Survey, in cooperation with the Delaware Department of Natural Resources and Environmental Control and the Delaware Geological Survey, conducted a groundwater-quality investigation to (1) describe the occurrence and distribution of PFAS, and (2) document any changes in groundwater quality in the Columbia aquifer public water-supply wells in the Delaware Coastal Plain between 2000 and 2008 and between 2008 and 2018. Thirty public water-supply wells located throughout the Columbia aquifer of the Delaware Coastal Plain were sampled from August through November 2018. Groundwater collected from the wells was analyzed for the occurrence and distribution of 18 per- and polyfluorinated alkyl substances (PFAS) as well as groundwater age. Descriptive statistical analyses were performed to assess PFAS analytical results within the well network and the combined perfluorooctanoic acid (PFOA) and perfluorooctane sulfonate (PFOS) concentrations were compared to the U.S. Environmental Protection Agency’s (EPA) health advisory level (HAL) for informational purposes only and not for evidence of compliance or noncompliance with Federal regulations. The EPA’s HAL is a health-based reference level for public drinking water as supplied to customers and is not applied to source (raw) water. Groundwater-age data were compared for sites sampled in 2000, 2008, and 2018 to document any changes.
All samples were analyzed for 18 PFAS using EPA Method 537 (modified). Forty-four percent of the analyzed PFAS were detected in the study well network. Sixteen of the sampled wells have one or more PFAS detections, and as many as eight different PFAS were found in a single sample. Wells with a higher number of PFAS detected (five or more) were in New Castle and Sussex Counties. The PFAS most frequently detected were PFOA, with 47 percent detection; perfluorohexanoic acid (PFHxA), with 33 percent detection; and PFOS and perfluorohexane sulfonate (PFHxS), with 27 percent detection each. PFAS concentrations were below 1,000 parts per trillion (ppt). Two wells exceeded the EPA’s lifetime-drinking water health advisory level of 70 ppt for combined concentrations of PFOA and PFOS.
The average age of groundwater entering the screens of the supply wells sampled in 2018 ranged from 8.2 to 45.8 years, with a median groundwater age of 25.7 years. Groundwater age was positively correlated with well depth and negatively correlated with dissolved oxygen. Groundwater age and PFAS concentrations were negatively correlated in the Columbia aquifer. Data from the 23 resampled wells indicate a significant positive difference in the average modeled groundwater-sample-age results. The average groundwater age from samples collected in 2018 was generally 5 years older than the average groundwater age from samples collected in 2008. The same pattern was found during cycle two (2008) of this study, where the 2008 groundwater age was on average 7 years older than the samples collected in 2000. The distribution of groundwater sample ages among the 17 trend wells and during the three study cycles (2000, 2008, and 2018) indicates that sample-age medians were statistically different from zero; well-water sample-age data show a slight increase in groundwater sample age.
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\"}}]}","contact":"Director, Maryland-Delaware-D.C. Water Science Center
U.S. Geological Survey
5522 Research Park Drive
Catonsville, MD 21228
Leavenworth Creek, a tributary of South Clear Creek and Clear Creek near Georgetown, Colorado, contains copper, lead, and zinc at concentrations close to or in excess of aquatic-life standards. In the summer of 2012, the U.S. Geological Survey, in cooperation with the U.S. Department of Agriculture Forest Service and the Colorado Division of Reclamation, Mining and Safety, conducted monitoring to (1) quantify the effects of diel cycling and perform synoptic sampling in a way to minimize those effects, (2) separate “point” or distinct single tributaries or sources of load from diffuse load sources along the study reach to aid remediation planning, and (3) quantify metal loading from transmountain diversion of water from Peru Creek through the Vidler Tunnel into Leavenworth Creek. The study included monitoring for diel cycles in June 2012 and diel and synoptic sampling in August 2012 along an approximately 2-kilometer stream reach. Synoptic samples were collected at 26 stream and 35 inflow, tributary, mine waste seep, and mine tunnel sites from August 28 to 30, 2012.
In June 2012, temperature, dissolved oxygen, and pH showed strong diel signals at two sites in Leavenworth Creek, with temperature and pH having minimum values near dawn and maximum values during the afternoon and dissolved oxygen having maximum values in the early morning and minimum values in late afternoon. Concentrations of zinc, cadmium, cobalt, manganese, and yttrium showed strong diel fluctuations at both sites with minimum concentrations during daytime and maximum concentrations during nighttime. Because of these diel cycles, all stream sites were sampled during synoptic sampling at 1200 hours on August 30, 2012. During synoptic sampling from August 28 to 30, 2012, zinc showed maximum concentrations at nighttime and minimum concentrations at midday and diel variation ranged from 26 to 33 percent.
Inflows from the Wilcox Tunnel and Waldorf seep area were the greatest source of zinc load to the stream (about 45 percent), and a left-bank inflow in the dispersed tailings area was the greatest source of lead (about 45 percent) and manganese (about 25 percent) loads to the stream, and a secondary source for zinc (about 40 percent). Copper load was almost equally divided (about 35 percent) between these two sources. Diffuse loading, likely from left-bank sources, was evident for copper, lead, manganese, and zinc in the stream reach from approximately 800 to 1,200 meters, and for copper, lead, and, to a lesser extent, manganese in the reach containing left-bank dispersed tailings (from approximately 1,300 to 1,800 meters). The load values reported herein are minimum estimates because the stream synoptic samples were collected at 1200 hours when positively charged elements, including copper, lead, manganese, and zinc, have minimum concentrations. Diel patterns measured for zinc during the synoptic sampling indicate maximum daily zinc loads were as much as 33 percent greater than those measured at 1200 hours on August 30, 2012.
Transmountain diversion of water through Vidler Tunnel negatively affects water quality in Leavenworth Creek as indicated by much greater metal loads and concentrations and a visually evident mixing zone where Vidler Tunnel water joins Leavenworth Creek when diversion is active compared to when it is not.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/ofr20211078","collaboration":"Prepared in cooperation with the U.S. Department of Agriculture Forest Service and the Colorado Division of Reclamation, Mining and Safety","usgsCitation":"Walton-Day, K., Runkel, R.L., Smith, C.D., and Kimball, B.A., 2021, Quantification of metal loading using tracer dilution and instantaneous synoptic sampling and importance of diel cycling in Leavenworth Creek, Clear Creek County, Colorado, 2012: U.S. Geological Survey Open-File Report 2021–1078, 37 p., https://doi.org/10.3133/ofr20211078.","productDescription":"Report: viii, 37 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-102543","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":392247,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9HGC2V4","text":"USGS data release","linkHelpText":"Stream discharge, sodium, bromide, and specific conductance data for stream and hyporheic zone samples affected by injection of sodium bromide tracer, Leavenworth Creek, Clear Creek County, Colorado, August 2012"},{"id":392246,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1078/ofr20211078.pdf","text":"Report","size":"5.21 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1078"},{"id":392245,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1078/coverthb.jpg"}],"country":"United States","state":"Colorado","county":"Clear Creek County","otherGeospatial":"Leavenworth Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -105.86219787597655,\n 39.595371402863655\n ],\n [\n -105.69602966308594,\n 39.595371402863655\n ],\n [\n -105.69602966308594,\n 39.71405356154611\n ],\n [\n -105.86219787597655,\n 39.71405356154611\n ],\n [\n -105.86219787597655,\n 39.595371402863655\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Colorado Water Science Center
U.S. Geological Survey
Box 25046, MS 415
Denver, CO 80225
Managers of our Nation’s resources face unprecedented challenges driven by the convergence of increasing, competing societal demands and a changing climate that affects the stability, vulnerability, and predictability of those resources. To help meet these challenges, the scientific community must take advantage of all available technologies, data, and integrative Earth systems modeling capacity to better inform resource and risk management decisions. This is the overarching goal of the U.S. Geological Survey (USGS) Earth Monitoring, Analysis, and Prediction (EarthMAP) vision: “By 2030, the USGS will deliver well integrated observations and predictions of the future state of natural systems—water, ecosystems, energy, minerals, hazards—at regional and national scales, working primarily with federal, state, and academic partners to develop and operate the capability” (U.S. Geological Survey, 2021).
Providing more integrated Earth systems science and actionable information to decision makers, stakeholders, and the public requires a better understanding of the depth and distribution of existing capacity (capabilities, tools, and techniques) across the Bureau. Identifying existing capacity is also a critical first step toward gap analysis and targeted investments to increase capacity over time. The USGS formed a Capacity Assessment Team (CAT) and charged it with (1) conducting a Request for Information (RFI) to identify existing USGS expertise and activities supportive of integrated and predictive science to inform decision making, (2) developing a strategy and proof-of-concept for a continuously updated capacity assessment capability, and (3) identifying lessons learned to inform development of best practices for future capacity assessment efforts.
The RFI took the form of a survey, with content guided by the science and technology needs identified in a USGS report titled “Grand Challenges for Integrated U.S. Geological Survey Science—A Workshop Report” (Jenni and others, 2017). The 44-question survey provided respondents the ability to rate their level of experience with a suite of priority disciplines, analysis and modeling approaches, technologies, and stakeholder engagement strategies and to enter optional narrative text for supporting context. An introductory portion focused on general science capacity assessment, followed by three sections targeting capabilities related to the foundational components of EarthMAP: (1) data and information integration, (2) integrated predictive science, and (3) actionable information.
The survey results provided a high-level snapshot of USGS capacity in the targeted areas. Respondents (1,035 individuals) represented approximately 13 percent of the USGS across all mission areas and regions. Seventy-four percent of the respondents held a science-focused position title and the remainder had position titles in information technology, computer science, management, administrative, or other (contractors, volunteers, emeritus, and unknown). To provide greater insight into respondent capabilities and activities, information from the U.S. Department of the Interior and USGS enterprise information systems were used to further characterize topical expertise and organizational associations of survey respondents. To address the ongoing need to assess the Bureau’s capacity to address integrated predictive science priorities, the CAT developed a software-based proof-of-concept called the Integrated Science Assessment Information Database (iSAID) for assembling various information sources together toward making the full extent of USGS capabilities and scientific assets available for routine capacity assessment. This proof-of-concept is intended to serve as a catalyst for further development. The process of implementing the EarthMAP capacity assessment survey, analyzing survey responses, and developing the proof-of-concept resulted in lessons learned, findings, and recommendations. Example scenarios throughout the report demonstrate how capacity assessment data can inform science planning. Three overarching findings and recommendations are:
(1) Finding: Capacity is limited in some critical disciplines, skills, and technology applications, but “sufficient” depends on the question and the need relative to availability at a given point in time.
Recommendation: Develop an on-demand capacity assessment framework that enables rapid identification and evaluation of existing and available expertise to support decision needs as they arise.
(2) Finding: Institutional barriers and lack of awareness constrain the ability of USGS staff to adopt new technologies, collaborate across administrative boundaries, and deliver actionable information to stakeholders in a timely manner. However, these barriers are not universally experienced.
Recommendation: Pursue more targeted inquiries to clarify which institutional barriers are obstructing the adoption of new technologies and approaches or the sharing of expertise and equipment across organizational and regional boundaries. These inquiries should inform USGS leadership, mission areas, and regions whether policies can be revised or whether a lack of understanding is creating perceived obstacles. Highlight cases when staff have successfully adopted new technologies and approaches to advance EarthMAP priorities and provide actionable information in a timely manner to spread awareness of how perceived obstacles can be navigated and overcome when appropriate.
(3) Finding: Examples of people and projects integrating across disciplines and scales and applying advanced approaches to meet complex stakeholder needs exist. Such examples provide transfer value across the spectrum from approach to decision making. Many projects, already underway, appear to meet elements of the EarthMAP vision, and the USGS has people who can provide leadership in multiple types of specific integrated science efforts.
Recommendation: Use these findings as a starting point for near-term strategic planning for integrated science. Highlight, incentivize, and build on existing interdisciplinary predictive science and information delivery activities across the USGS to advance toward further realization of an EarthMAP capacity.
The CAT efforts to develop and assess existing USGS capacity to advance the EarthMAP vision revealed a fundamental challenge for not only this effort but any effort to assess existing capacity: A considerable amount of thought, time, and effort is required to survey and assess capabilities and tools available to support a given need, yet best results are still likely to provide an incomplete assessment. To better meet the frequent need to assess capabilities, tools, products, and projects that address an expressed strategic priority, the CAT proposes the concept of an on-demand capacity assessment framework supported by a software package that dynamically pulls and integrates information from existing USGS information systems and public domain registries. Although existing USGS enterprise information systems currently lack the structure, cross-system consistency, interoperability, and stability to support a continuously updated capacity assessment capability, we identify reasonable near-term steps to improve the utility of information gathered on expertise and project capacity and to improve the consistency and completeness of information and the ability of USGS systems to share that information. The ability to search and characterize this information will make future assessments of capacity faster, more complete, more efficient, and more targeted. This approach would grow the Bureau’s capacity knowledge over time, iteratively improving the ability to access, leverage, and synthesize existing capabilities and assets as well as identify and fill critical gaps. The greatest promise for developing integrated science could lie in linking across existing projects and expertise to create a multi-project capacity for addressing large, complex environmental issues.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211102","usgsCitation":"Keisman, J.L., Bristol, S., Brown, D.S., Flickinger, A.K., Gunther, G., Murdoch, P.S., Musgrove, M., Nelson, J.C., Steyer, G.D., Thomas, K.A., and Waite, I.R., 2021, Capacity assessment for Earth Monitoring, Analysis, and Prediction (EarthMAP) and future integrated monitoring and predictive science at the U.S. Geological Survey: U.S. Geological Survey Open-File Report 2021-1102, 110 p., https://doi.org/10.3133/ofr20211102.","productDescription":"Report: v, 110 p.; Data Release","numberOfPages":"110","onlineOnly":"Y","ipdsId":"IP-129970","costCenters":[{"id":208,"text":"Core Science Analytics and Synthesis","active":true,"usgs":true},{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true},{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true},{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true},{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true},{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true},{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true},{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"links":[{"id":392008,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9BB5NMZ","linkHelpText":"USGS Earthmap Capacity Assessment Dataset"},{"id":392006,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2021/1102/images"},{"id":392005,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2021/1102/ofr20211102.xml"},{"id":392004,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1102/ofr20211102.pdf","text":"Report","size":"6 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":392003,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1102/covrthb.jpg"}],"contact":"Director,
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
Upper Esopus Creek is the primary tributary to the Ashokan Reservoir, part of the New York City water-supply system. Elevated concentrations of suspended sediment and turbidity in the watershed of the creek are of concern for the system.
Water samples were collected through a range of streamflow and turbidity at 14 monitoring sites in the upper Esopus Creek watershed for analyses of suspended-sediment concentration (SSC) and measurements of turbidity. Analyses of the samples provided data that were used to develop cross-section coefficients and turbidity-SSC regression equations for the monitoring sites for the period October 2016 through September 2019. The equations can be used to estimate SSC at a 15-minute timestep for the monitored sites. The equations can be validated for future use by the collection and analysis of additional data.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211065","collaboration":"Prepared in cooperation with the New York City Department of Environmental Protection","usgsCitation":"Siemion, J., Bonville, D.B., McHale, M.R., and Antidormi, M.R., 2021, Turbidity–suspended-sediment concentration regression equations for monitoring stations in the upper Esopus Creek watershed, Ulster County, New York, 2016–19: U.S. Geological Survey Open-File Report 2021–1065, 27 p., https://doi.org/10.3133/ofr20211065.","productDescription":"Report: vi, 27 p.; Data Release","numberOfPages":"27","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-120199","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":391805,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9MV3NZ8","text":"USGS data release","linkHelpText":"Suspended-sediment concentration and turbidity data for sites in the upper Esopus Creek watershed New York, 2016–19"},{"id":391807,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2021/1065/images"},{"id":391804,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1065/ofr20211065.pdf","text":"Report","size":"2.19 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1065"},{"id":391806,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2021/1065/ofr20211065.XML"},{"id":391803,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1065/coverthb.jpg"}],"country":"United States","state":"New York","county":"Ulster County","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-73.9109,42.1271],[-73.916,42.1199],[-73.9245,42.1019],[-73.9311,42.082],[-73.93,42.0765],[-73.9302,42.0679],[-73.9341,42.0575],[-73.937,42.0398],[-73.9347,42.0293],[-73.9331,42.0216],[-73.9436,41.9913],[-73.9504,41.9664],[-73.9556,41.9528],[-73.9551,41.9464],[-73.954,41.9401],[-73.9567,41.9301],[-73.9625,41.9179],[-73.9639,41.9138],[-73.9609,41.9088],[-73.9423,41.8827],[-73.9389,41.8704],[-73.939,41.8654],[-73.9423,41.8596],[-73.9448,41.8559],[-73.9461,41.851],[-73.9477,41.8346],[-73.9463,41.8142],[-73.9504,41.7979],[-73.9488,41.7847],[-73.946,41.7719],[-73.9414,41.7592],[-73.9408,41.7592],[-73.938,41.7469],[-73.9389,41.7337],[-73.9424,41.7142],[-73.9439,41.6993],[-73.9411,41.6884],[-73.9513,41.6149],[-73.9525,41.59],[-73.9999,41.5855],[-74.0521,41.5816],[-74.0575,41.5926],[-74.0677,41.604],[-74.0886,41.5988],[-74.0983,41.6089],[-74.1246,41.6133],[-74.1325,41.6152],[-74.1282,41.5833],[-74.1858,41.5944],[-74.187,41.5908],[-74.1907,41.5913],[-74.2458,41.6036],[-74.25,41.6059],[-74.2502,41.6291],[-74.2606,41.6337],[-74.2667,41.6324],[-74.2754,41.6284],[-74.281,41.6257],[-74.2989,41.6182],[-74.3156,41.6115],[-74.3187,41.6084],[-74.3404,41.5954],[-74.3521,41.5982],[-74.3583,41.5938],[-74.3675,41.5916],[-74.3681,41.5961],[-74.3705,41.597],[-74.3736,41.5975],[-74.376,41.5994],[-74.3772,41.6044],[-74.3807,41.6117],[-74.3843,41.6167],[-74.3873,41.6217],[-74.3884,41.6299],[-74.392,41.6345],[-74.3926,41.6399],[-74.3943,41.6458],[-74.4004,41.6486],[-74.4449,41.6726],[-74.4833,41.6942],[-74.5755,41.7453],[-74.4892,41.8377],[-74.4573,41.8747],[-74.5124,41.8992],[-74.6363,41.9542],[-74.7235,41.9915],[-74.78,42.0182],[-74.667,42.0697],[-74.5538,42.1212],[-74.5312,42.1464],[-74.504,42.1449],[-74.4516,42.1694],[-74.3077,42.1142],[-74.2496,42.1095],[-74.0767,42.0968],[-74.0424,42.1682],[-74.0259,42.1621],[-74.0054,42.1642],[-74.0038,42.18],[-73.9189,42.1286],[-73.9109,42.1271]]]},\"properties\":{\"name\":\"Ulster\",\"state\":\"NY\"}}]}","contact":"Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349
Anas acuta (Northern pintail; hereafter pintail) was selected as a model species on which to base a decision-support framework linking regional actions to continental-scale population and harvest objectives. This framework was then used to engage stakeholders, such as Landscape Conservation Cooperatives’ (LCCs’) habitat management partners within areas of importance to pintails, while maximizing cross-taxa effects from the framework. The mathematical framework for the model had been previously developed for pintails. A key assumption incorporated into the model is that density dependence in survival occurs during the post-hunting (winter) period, where resources are hypothesized to be limiting. Because few data are available to directly inform this process, the approach used was to build a hierarchical Bayesian integrated population model (IPM) that simultaneously uses data from bird-band recoveries, breeding population counts, and harvest surveys to estimate values of parameters of an annual population projection model, including population size, survival rate, reproductive rate, and process and observation error variances, that are logically consistent with each other, given the mathematical structure imposed through the IPM.
The main accomplishments of this study are (1) development of an IPM for pintail to guide harvest and habitat management, (2) development of a Prairie Parkland Region breeding submodel to predict pintail productivity, (3) development of statistical methodology to estimate pintail productivity (as measured by the ratio of juvenile to adults in hunter-collected wing samples) and winter survival and to relate these estimates to covariates, and (4) illustration of how to use a model and estimated parameter values to predict pintail population size and sustainable harvest as a function of habitat.
Estimation of pintail survival from bird-banding data shows that there has been relatively little variation in survival over the period 1960–2013. A productivity model showed strong effects of breeding ground conditions, wintering-ground precipitation, and density dependence on pintail productivity. Thus, most temporal variation in pintail demographic rates has been due to effects on reproduction and not survival, including effects of breeding or wintering-ground habitat. These results indicate that habitat conservation efforts may be most effective if they focus on maintaining or increasing breeding and wintering-ground habitat to increase pintail productivity rather than pintail survival. Environmental perturbations in excess of historical experience, such as what could occur under climate change, might have meaningful effects on survival but cannot be estimated with current data. Direct effects of climate, land use, or management are likely to be greater on productivity than survival, but substantial uncertainty remains about predictions of equilibrium population size and sustainable yield.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201084","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service","usgsCitation":"Osnas, E.E., Boomer, G.S., Devries, J.H., and Runge, M.C., 2021, Decision-support framework for linking regional-scale management actions to continental-scale conservation of wide-ranging species: U.S. Geological Survey Open-File Report 2020–1084, 31 p., https://doi.org/10.3133/ofr20201084.","productDescription":"Report: vi, 31 p.; Data Release","numberOfPages":"31","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-083951","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":391433,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P93YTR3X","text":"USGS data release","linkHelpText":"Data release—Decision-support framework for linking regional-scale management actions to continental-scale conservation of wide-ranging species"},{"id":391431,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1084/coverthb.jpg"},{"id":391432,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1084/ofr20201084.pdf","text":"Report","size":"6.72 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1084"}],"contact":"Director, Eastern Ecological Science Center
U.S. Geological Survey
12100 Beech Forest Road
Laurel, MD 20708
The Digital Shoreline Analysis System version 5 software is an add-in to Esri ArcGIS Desktop version 10.4–10.7 that enables a user to calculate rate-of-change statistics from a time series of vector shoreline positions. The Digital Shoreline Analysis System provides an automated method for establishing measurement locations, performs rate calculations, provides the statistical data necessary to assess the reliability of the rates, and includes a beta model for forecasting shoreline position. The Digital Shoreline Analysis System version 5.1 includes updates to the interface and the application of proxy-datum bias. This in-depth user guide provides comprehensive instruction on the installation and use of the program, including how to create a reference baseline for measurements, steps needed to generate measurement transects and metadata, guidelines on how to manually add or edit existing transects, and an explanation of the visualization options to display calculated rates of shoreline change.
Note: As of May 2024, the current version of the Digital Shoreline Analysis System (DSAS), version 6.0, is a standalone desktop application for calculating shoreline or boundary change over time. The user guide for DSAS version 5.1 is applicable to many aspects of version 6.0. The user guide provides relevant information on the DSAS workflow, including how to define a reference baseline for measurements, attribute requirements for baselines and shorelines, and supporting information on rate calculations and statistics.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211091","usgsCitation":"Himmelstoss, E.A., Henderson, R.E., Kratzmann, M.G., and Farris, A.S., 2021, Digital Shoreline Analysis System (DSAS) version 5.1 user guide: U.S. Geological Survey Open-File Report 2021–1091, 104 p., https://doi.org/10.3133/ofr20211091.","productDescription":"Report: xi, 104 p.; Software Release","numberOfPages":"104","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-123671","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":390774,"rank":4,"type":{"id":18,"text":"Project Site"},"url":"https://www.usgs.gov/centers/whcmsc/science/digital-shoreline-analysis-system-dsas","text":"Digital Shoreline Analysis System (DSAS)"},{"id":390775,"rank":3,"type":{"id":35,"text":"Software Release"},"url":"https://doi.org/10.5066/P13WIZ8M","text":"Digital Shoreline Analysis System version 6.0"},{"id":390767,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1091/ofr20211091.pdf","text":"Report","size":"11.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1091"},{"id":390766,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1091/coverthb.jpg"}],"contact":"Director, Woods Hole Coastal and Marine Science Center
U.S. Geological Survey
384 Woods Hole Road
Quissett Campus
Woods Hole, MA 02543–1598
During September and October 2019, the U.S. Geological Survey mapped the shoreface and inner continental shelf offshore of the Rockaway Peninsula in New York using high-resolution chirp seismic reflection and single-beam bathymetry geophysical techniques. The results from this study are important for assessing the Quaternary evolution of the Rockaway Peninsula and determining coastal sediment availability, which is crucial for establishing sediment budgets, understanding sediment dispersal, and managing coastlines. This report presents preliminary interpretations of seismic profiles and maps of shoreface and Holocene sediment thickness from the shoreline to about 2 kilometers offshore. The results indicate that shoreface and Holocene sediment thickness demonstrates zonal variability because of underlying geology and sediment availability. Based on geomorphic features and underlying stratigraphy, the study area is separated into west, west-central, east-central, and east zones. Holocene sediment, which includes the shoreface and seafloor features with positive morphology (for example, nearshore bars, ebb-tide deltas, and sorted bedforms), thickens to the west and may be related to accommodation and westward dip of the regional unconformity. Shoreface units, which are thought to represent the active volume of littoral sediment, are thickest in the west-central peninsula where the geologic base of the shoreface is deeper. Shoreface units with moderate thickness are in the western and eastern peninsula where there are positive morphological features (for example, deposits accumulating updrift from the jetty, ebb-tide deltas, and so on). The thinnest shorefaces are in the east-central Rockaway Peninsula because of less accommodation caused by the shoaling regional unconformity.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211100","collaboration":"Prepared in cooperation with the National Fish and Wildlife Foundation","usgsCitation":"Wei, E.A., Miselis, J.L., and Forde, A.S., 2021, Shoreface and Holocene sediment thickness offshore of Rockaway Peninsula, New York: U.S. Geological Survey Open-File Report 2021–1100, 14 p., https://doi.org/10.3133/ofr20211100.","productDescription":"Report: iv, 14 p.; 2 Data Releases","numberOfPages":"14","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-125818","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":391426,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20211100/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":391345,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2021/1100/images/"},{"id":391343,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ZO8QKJ","linkHelpText":"Archive of chirp subbottom profile data collected in 2019 from Rockaway Peninsula, New York"},{"id":391346,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2021/1100/ofr20211100.XML"},{"id":391344,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9WNJSFN","linkHelpText":"Coastal bathymetry and backscatter data collected in September and October 2019 from Rockaway Peninsula, New York"},{"id":391342,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1100/ofr20211100.pdf","text":"Report","size":"11.0 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1100"},{"id":391341,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1100/coverthb.jpg"}],"country":"United States","state":"New York","otherGeospatial":"Rockaway Peninsula","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -73.76152038574219,\n 40.57067539946112\n ],\n [\n -73.74229431152344,\n 40.593620934177494\n ],\n [\n -73.76083374023438,\n 40.59414233212419\n ],\n [\n -73.82469177246094,\n 40.58527801407785\n ],\n [\n -73.8885498046875,\n 40.563372896916164\n ],\n [\n -73.92974853515625,\n 40.549287249082035\n ],\n [\n -73.94622802734375,\n 40.53937335015618\n ],\n [\n -73.9441680908203,\n 40.529979881843865\n ],\n [\n -73.92974853515625,\n 40.526326510744006\n ],\n [\n -73.883056640625,\n 40.53311118427234\n ],\n [\n -73.83018493652344,\n 40.54772199417569\n ],\n [\n -73.77388000488281,\n 40.56389453066509\n ],\n [\n -73.76152038574219,\n 40.57067539946112\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
This report provides a step-by-step method for compiling hydraulic fracturing data in the United States from the IHS Markit, 2019, U.S. Well History and Production Relational Database. Data on hydraulically fractured wells include their location (geologic province, State, county), well type (oil or gas), orientation (directional, horizontal, or vertical), spud date, completion date and the hydraulic fracturing treatments, treatment fluids types, treatment fluid volumes, additive types, agent types (“proppants”), and proppant amounts injected. This method also describes how to associate each unique well with the hydraulic fracturing treatments to provide an indication of the total amount of all treatment fluids injected into a well for hydraulic fracturing and the volume of each individual treatment fluid type injected.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211090","usgsCitation":"Varela, B.A., and Gallegos, T.J., 2021, Method for compiling temporally and spatially aggregated data on hydraulic fracturing—Treatments and wells: U.S. Geological Survey Open-File Report 2021–1090, 30 p., https://doi.org/10.3133/ofr20211090.","productDescription":"vi, 30 p.","numberOfPages":"30","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-118145","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true},{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":436123,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P933SF16","text":"USGS data release","linkHelpText":"Spatial and Temporal Data on Hydraulic Fracturing Fluid Types and Amounts Injected into Oil and Gas Wells Across the U.S., 2015-2019"},{"id":391203,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1090/coverthb.jpg"},{"id":391204,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1090/ofr20211090.pdf","text":"Report","size":"730 KB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1090"}],"contact":"Geology, Energy and Minerals Science Center
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192