Eelgrass (<em>Zostera marina</em>) is a highly productive seagrass that plays an essential role in the health of the estuarine and coastal ecosystems; however, information about its abundance and distribution is insufficient in the Bering Sea along the Yukon Delta National Wildlife Refuge. We inventoried the spatial extent and abundance of eelgrass and seaweed in Duchikthluk and Shoal bays on Nunivak Island in July 2010. Using Landsat Thematic Mapper imagery, we estimated the spatial extent of eelgrass to be 1,232 hectares in Duchikthluk Bay and 40 hectares in Shoal Bay. The overall accuracy of the assessments was high (86–87 percent) based on ground truthing using field reference points. We used point-sampling methodology to assess eelgrass abundance relative to the presence of associated seaweeds and selected macro-invertebrates within each of bays. Eelgrass was found at water depths ranging from 0.1 to 2.9 meters across both bays, but the greatest density (>75 percent cover) occurred primarily in moderate to deep water (0.7–1.4 meters) in Duchikthluk Bay and deeper water (>2 meters) in Shoal Bay. The mean aboveground biomass was 39.4±4.0 grams per meter squared in Duchikthluk Bay. The eelgrass biomass was greater (67.6±11.0 grams per meter squared) in Shoal Bay, but this estimate was based on a small sample size (n=3). Seaweeds, representing six species, occurred in low abundance across both bays and were primarily associated with eelgrass. Gastropods were the most common macro-invertebrate, occurring at 45 percent of field points in Duchikthluk Bay.
Director, Alaska Science Center
U.S. Geological Survey
4210 University Drive
Anchorage, Alaska 99508
We conducted a point-sampling survey to determine eelgrass (Zostera marina) and seaweed abundance in coastal waters adjacent to Togiak National Wildlife Refuge, Alaska, in July 2008–10. Eelgrass was known to be abundant in protected embayments of the southeastern Bering Sea and near the Togiak National Wildlife Refuge, but prior to this study, no systematic ground surveys had been conducted in these areas. We determined mean aboveground biomass of eelgrass to be highly variable among years observed, ranging from 32–72 grams dry weight per square meter (g/m2) during successive years in Nanvak Bay and among the studied embayments in 2010: 47±4 g/m2 in Nanvak Bay, 69±7 g/m2 in Chagvan Bay, and 74±15 g/m2 in Goodnews Bay. Seaweed density, abundance, and frequency scores were also highly variable among years and among embayments and were lower for seaweeds than for eelgrass in Nanvak and Chagvan bays, but not in Goodnews Bay. For all bays, mussels (Mytilus spp.) and gastropods were the most common macro-invertebrates detected during surveys, whereas sea stars, crabs, and sponges were not observed in the embayments.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201114","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service","usgsCitation":"Ward, D.H, Hogrefe, K.R, Swaim, M.A., Donnelly, T.F., and Fairchild, L.L., 2022, Eelgrass (Zostera marina) and Seaweed Abundance along the Coast of Togiak National Wildlife Refuge, Alaska, 2008–10: U.S. Geological Survey Open-File Report 2020–1114, 14 p., https://doi.org/10.3133/ofr20201114.","productDescription":"Report: v, 14 p.; 2 Data Releases","onlineOnly":"Y","ipdsId":"IP-117779","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":405817,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P92BMFTH","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 Togiak National Wildlife Refuge, Alaska"},{"id":405818,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9WEK4JI","text":"USGS data release","description":"USGS data release","linkHelpText":"Mapping data of eelgrass (Zostera marina) distribution, Alaska and Baja California, Mexico"},{"id":405822,"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":405815,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1114/coverthb.jpg"},{"id":405816,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1114/ofr20201114.pdf","text":"Report","size":"2.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1114"},{"id":405819,"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":405820,"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"},{"id":405821,"rank":7,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20201144","text":"OFR 2020-1144 —","description":"OFR 2020-1144","linkHelpText":"Eelgrass (Zostera marina) and seaweed assessment Alaska Peninsula-Becharof National Wildlife Refuges, 2010"},{"id":405823,"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"}],"country":"United States","state":"Alaska","otherGeospatial":"Togiak 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 -162.25,\n 58.5\n ],\n [\n -160,\n 58.5\n ],\n [\n -160,\n 59.25\n ],\n [\n -162.25,\n 59.25\n ],\n [\n -162.25,\n 58.5\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Alaska Science Center
U.S. Geological Surveyr
4210 University Driver
Anchorage, Alaska 99508
Declines in the distribution and abundance of seagrasses worldwide have prompted a need for baseline distribution maps of eelgrass (Zostera marina) in Alaska. We used high-resolution digital-color aerial photography and multi-spectral satellite imagery to map the distribution and spatial extent of eelgrass at 21 sites in coastal waters adjacent to Togiak National Wildlife Refuge (TNWR) in northwestern Bristol Bay and southern Kuskokwim Bay. The total spatial extent of eelgrass meadows was estimated to be 6,489 hectare (ha) almost equally divided between Bristol Bay (3,001 ha) and Kuskokwim Bay (3,488 ha). The four largest eelgrass beds occurred in Chagvan Bay (1,933 ha), the north side of Hagemeister Island (1,168 ha), Goodnews Bay (874 ha), and Nanvak Bay (599 ha). This report provides key baseline data useful for establishing a monitoring plan to assess trends in eelgrass along the coast of TNWR.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201080","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service","usgsCitation":"Ward, D.H., Hogrefe, K.R., Donnelly, T.F., and Swaim, M.A., 2022, Distribution of eelgrass (Zostera marina) in coastal waters adjacent to Togiak National Wildlife Refuge, Alaska: U.S. Geological Survey Open-File Report 2020–1080, 21 p., https://doi.org/10.3133/ofr20201080.","productDescription":"Report: v, 21 p.; 2 Data Releases","onlineOnly":"Y","ipdsId":"IP-114072","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":384513,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P92BMFTH","text":"USGS data release","description":"USGS data release","linkHelpText":"Point sampling data for eelgrass (Zostera marina) abundance adjacent to the Togiak National Wildlife Refuge, Alaska"},{"id":384512,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1080/ofr20201080.pdf","text":"Report","size":"4.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1080"},{"id":384511,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1080/coverthb1.jpg"},{"id":405745,"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":405747,"rank":6,"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":405748,"rank":7,"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":405746,"rank":8,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20201144","text":"OFR 2020-1144 —","description":"OFR 2020-1144","linkHelpText":"Eelgrass (Zostera marina) and seaweed assessment Alaska Peninsula-Becharof National Wildlife Refuges, 2010"},{"id":384514,"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":405749,"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"}],"country":"United States","state":"Alaska","otherGeospatial":"Togiak 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 -162.25,\n 58.5\n ],\n [\n -159.75,\n 58.5\n ],\n [\n -159.75,\n 59.25\n ],\n [\n -162.25,\n 59.25\n ],\n [\n -162.25,\n 58.5\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Alaska Science Center
U.S. Geological Survey
4210 University Drive
Anchorage, Alaska 99508
Eelgrass (Zostera marina) meadows are expansive along the lower Alaska Peninsula, supporting a rich diversity of marine life, yet little is known about their status and trends in the region. We tested techniques to inventory and monitor trends in the spatial extent and abundance of eelgrass in lagoons of the Izembek National Wildlife Refuge. We determined if Landsat imagery could be used to assess eelgrass spatial extent in shallow (less than 4 meter water depth) coastal waters of the refuge. We determined that this seagrass could be differentiated using Landsat imagery from other cover types (that is, channels and unvegetated tidal flats) with a high degree of accuracy (greater than 80 percent) in Izembek and Kinzarof Lagoons. Eelgrass meadows represented the largest cover type in Izembek (about 16,000 hectares) and Kinzarof (about 900 hectares) Lagoons, comprising between 45 and 50 percent of the spatial extent of these lagoons, respectively. When compared to estimates of spatial extent of eelgrass from previous studies, our results suggest little change in the spatial extent of eelgrass in Izembek Lagoon during the 28-year period 1978 through 2006. Preliminary mapping of eelgrass in other embayments indicated that this seagrass was also expansive in Big Lagoon (about 900 hectares; or 34 percent of the lagoon area) and Hook Bay (about 900 hectares; or 36 percent of the bay area) but not in Cold Bay (about 100 hectares; less than 5 percent of the bay area). We conducted an embayment-wide point sampling technique to assess aboveground biomass and distribution of eelgrass and seaweeds and presence of six macro-invertebrates during a 4-year period (2007–10). We determined that, when present, mean aboveground biomass of eelgrass was greater in Kinzarof Lagoon (182.5 plus or minus 12.1 grams dry weight per square meter) than in Izembek Lagoon (152.1 plus or minus 7.1 grams dry weight per square meter) in 2008–10, possibly reflecting the warmer sea temperatures and higher salinities found on the Gulf of Alaska side of the Alaska Peninsula. Seaweeds were more abundant in Kinzarof Lagoon than in Izembek Lagoon, surpassing aboveground biomass of eelgrass in both lagoons in 2008. Gastropods (4 percent of all points) and Caprella shrimp (25 percent) were the most common of the six macro-invertebrates surveyed in Izembek Lagoon, and Telmessus crab was the most common macro-invertebrate in Kinzarof Lagoon.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201035","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., Sowl, K.M., and Lindstrom, S.C., 2022, Abundance and distribution of eelgrass (Zostera marina) and seaweeds at Izembek National Wildlife Refuge, Alaska, 2007–10: U.S. Geological Survey Open-File Report 2020–1035, 30 p., https://doi.org/10.3133/ofr20201035.","productDescription":"Report: vi, 30 p.; 2 Data Releases","onlineOnly":"Y","ipdsId":"IP-112900","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":384516,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ZUDIOH","text":"USGS data release","description":"USGS data release","linkHelpText":"Point sampling for eelgrass (Zostera marina) and seaweeds in embayments adjacent to the Izembek National Wildlife Refuge, Alaska"},{"id":384515,"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":435682,"rank":10,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XNSWES","text":"USGS data release","linkHelpText":"Sampling Data for Eelgrass (Zostera marina) in Norma Bay, Izembek Lagoon, Alaska, 1987"},{"id":405752,"rank":7,"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":405751,"rank":6,"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":379325,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1035/ofr20201035.pdf","text":"Report","size":"3.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1035"},{"id":405754,"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":405753,"rank":8,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20201144","text":"OFR 2020-1144 —","description":"OFR 2020-1144","linkHelpText":"Eelgrass (Zostera marina) and seaweed assessment Alaska Peninsula-Becharof National Wildlife Refuges, 2010"},{"id":384518,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1035/coverthb.jpg"},{"id":405750,"rank":5,"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":"Izembek 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 -163.37081909179688,\n 55.00755132274014\n ],\n [\n -162.77206420898438,\n 55.00755132274014\n ],\n [\n -162.77206420898438,\n 55.2963199179754\n ],\n [\n -163.37081909179688,\n 55.2963199179754\n ],\n [\n -163.37081909179688,\n 55.00755132274014\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Alaska Science Center
U.S. Geological Survey
4210 University Drive
Anchorage, Alaska 99508
A working group of experts with diverse professional backgrounds and disciplinary expertise was assembled to conceptualize a spatially explicit conservation design to support and inform the Sagebrush Conservation Strategy Part 2. The goal was to leverage recent advancements in remotely sensed landcover products to develop spatially and temporally explicit maps of sagebrush rangeland condition and landscape threats. In addition, the group sought to provide a common basis for understanding the state of sagebrush rangelands through time.
First, the study team developed a spatially explicit model to assess geographic patterns in sagebrush ecological integrity and used this model to identify core sagebrush areas (CSAs), growth opportunity areas (GOAs), and other rangeland areas (ORAs) across the biome. Among the identified rangelands, 33.4 million acres were classified as CSAs; 84.3 million acres as GOAs; and 127.2 million acres as ORAs as of 2020. Second, the team sought to demonstrate the ecological relevance of the identified CSAs and GOAs by comparing these data with independent datasets for sagebrush obligate species of conservation concern. Geographical patterns in sagebrush ecological integrity were strongly associated with the occurrence of high-priority species and also displayed clear links to population performance for greater sage-grouse. Third, the team parsed out the type, location, and acres of primary threats within the different categories (CSAs, GOAs, and ORAs) to help focus active management by identifying places where multiagency and organization efforts can protect CSAs and GOAs that have higher levels of integrity with lower cumulative threats. The assessment of the condition of the sagebrush biome (that is, the location, amount, and conservation status) indicated that complex ecosystem function problems are driving ~73 percent of the demonstrated threats within the CSAs and GOAs (rather than point-source problems, such as human development). Fourth, the team developed trend estimates for the identified CSAs and GOAs and three selected primary threats (invasive annual grasses, conifer encroachment, and human modification) to the sagebrush biome from 2001 to 2020. Results showed that an average of 1.3 million acres per year have transitioned to ORAs at an annual rate of −1.34 percent. Fifth, the team developed an approach to integrate climate change effects into the threat-based landscape conservation design and conducted an initial assessment on the magnitude of near-term climate effects in the context of observed historical trends. The team’s analysis suggests that climate change alone is unlikely to be the dominant threat to sagebrush ecological integrity in the next few decades, although interactions of climate with wildfire and invasive annual grasses may be an important threat, especially in the longer term.
A spatial overlap analysis was performed and highlighted 45.8 million acres of shared priorities among existing conservation frameworks to help anchor and guide collaborative landscape-scale conservation of areas that still have no to low threats. This information is critical to provide context for decisions about the volume and nature of conservation actions and funding requirements.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221081","collaboration":"Prepared in cooperation with the Western Association of Fish and Wildlife Agencies and the U.S. Fish and Wildlife Service","usgsCitation":"Doherty, K., Theobald, D.M., Bradford, J.B., Wiechman, L.A., Bedrosian, G., Boyd, C.S., Cahill, M., Coates, P.S., Creutzburg, M.K., Crist, M.R., Finn, S.P., Kumar, A.V., Littlefield, C.E., Maestas, J.D., Prentice, K.L., Prochazka, B.G., Remington, T.E., Sparklin, W.D., Tull, J.C., Wurtzebach, Z., and Zeller, K.A., 2022, A sagebrush conservation design to proactively restore America’s sagebrush biome: U.S. Geological Survey Open-File Report 2022–1081, 38 p., https://doi.org/10.3133/ofr20221081.","productDescription":"Report: viii, 38 p.; Data Release; 3 Figures: 7.99 × 6.10 inches or smaller","numberOfPages":"38","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-138940","costCenters":[{"id":506,"text":"Office of the AD Ecosystems","active":true,"usgs":true}],"links":[{"id":407103,"rank":5,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/of/2022/1081/ofr20221081_fig10.pdf","text":"Figure 10, full size","size":"5.46 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Conifer 2020"},{"id":407104,"rank":6,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/of/2022/1081/ofr20221081_fig11.pdf","text":"Figure 11, full size","size":"9.45 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Human Modification 2020"},{"id":407025,"rank":4,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/of/2022/1081/ofr20221081_fig09.pdf","text":"Figure 9, full size","size":"5.57 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Invasive Annual Grass 2020"},{"id":407023,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1081/ofr20221081.pdf","text":"Report","size":"32.4 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":407022,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1081/coverthb.jpg"},{"id":407024,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P94Y5CDV","text":"USGS data release","linkHelpText":"Biome-wide sagebrush core habitat and growth areas estimated from a threat-based conservation design"}],"country":"United States","otherGeospatial":"western United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.6845703125,\n 33\n ],\n [\n -101.25,\n 33\n ],\n [\n -101.25,\n 49\n ],\n [\n -121.6845703125,\n 49\n ],\n [\n -121.6845703125,\n 33\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Sagebrush Ecosystem Specialist
Land Management Research Program
Ecosystems Mission Area
U.S. Geological Survey
2150 Centre Ave., Bldg. C
Fort Collins, CO 80526
Biodiversity offsetting, or compensatory mitigation, is increasingly being used in temperate grassland and wetland ecosystems to compensate for unavoidable environmental damage from anthropogenic disturbances such as energy development and road construction. Energy-extraction and -generation facilities continue to proliferate across the natural landscapes of the United States, yet mitigation tools to ameliorate the negative behavioral effects on wildlife from these types of facilities are rarely implemented. Scientists from the U.S. Geological Survey conducted a 10-year before-after-control-impact (commonly referred to as BACI) study that evaluated the displacement effects of wind facilities on breeding grassland birds. The study determined behavioral avoidance for 7 of 9 species. This research is notable because of its design, geographical scope, and duration, which allowed for the determination of immediate, short-term effects; delayed or sustained effects; and discrete distances at which effects occurred. In addition, the U.S. Fish and Wildlife Service and Ducks Unlimited conducted a 3-year concurrent-year paired-reference study to determine behavioral avoidance for five species of dabbling ducks. By quantifying displacement rate from these two studies, U.S. Geological Survey and U.S. Fish and Wildlife Service scientists developed the Avian-Impact Offset Method (AIOM) to quantify and compensate for loss in value of breeding habitat. The AIOM converts the biological value (that is, number of bird pairs) lost by way of avoidance and estimates the site-specific number of hectares of grasslands and number of wetlands needed to compensate for displaced pairs of grassland birds and waterfowl. By converting biological value to traditional units of measure in which land is described and purchased or sold, the AIOM lends itself readily to the delivery of offsetting measures such as easement protections and restoration projects. The AIOM tool is applicable to wind, solar, oil, gas, and transportation infrastructure.
This tutorial was designed to increase awareness of the AIOM and to promote its proper application. The tutorial is divided into four sections, each of which explains a discrete topic concerning aspects of behavioral displacement. The first section provides geographical and biological context, and the second section describes the field and statistical methods and results. The third section provides step-by-step instructions for applying the AIOM to several scenarios involving grassland birds or waterfowl at wind or oil facilities. The fourth section describes decision-support tools created to implement the AIOM. The appendices provide the actual field protocols constituting the methods for the research, provide detailed results by species and wind facility for that research, and provide detailed instructions for downloading and applying the decision-support tools.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221049","collaboration":"Prepared in collaboration with the U.S. Fish and Wildlife Service","usgsCitation":"Shaffer, J.A., Loesch, C.R., and Buhl, D.A., 2022, Understanding the Avian-Impact Offset Method—A tutorial: U.S. Geological Survey Open-File Report 2022–1049, 227 p., https://doi.org/10.3133/ofr20221049.","productDescription":"Report: v, 227 p.; 2 Data Releases","numberOfPages":"238","onlineOnly":"Y","ipdsId":"IP-134338","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":406394,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1049/coverthb.jpg"},{"id":406396,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9J6QUF6","text":"USGS data release","linkHelpText":"North American Breeding Bird Survey dataset 1966–2019"},{"id":406395,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1049/ofr20221049.pdf","text":"Report","size":"95.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022–1049"},{"id":406397,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7T43SDG","text":"USGS data release","linkHelpText":"Effects of wind-energy facilities on breeding grassland bird distributions"}],"country":"Canada, United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -112.236328125,\n 52.10650519075632\n ],\n [\n -113.73046875,\n 52.16045455774706\n ],\n [\n -114.78515624999999,\n 51.17934297928927\n ],\n [\n -112.8515625,\n 48.922499263758255\n ],\n [\n -111.796875,\n 47.45780853075031\n ],\n [\n -107.40234375,\n 47.040182144806664\n ],\n [\n -103.798828125,\n 47.81315451752768\n ],\n [\n -102.39257812499999,\n 47.338822694822\n ],\n [\n -100.72265625,\n 45.82879925192134\n ],\n [\n -100.37109375,\n 44.5278427984555\n ],\n [\n -99.49218749999999,\n 43.32517767999296\n ],\n [\n -96.064453125,\n 41.96765920367816\n ],\n [\n -94.39453125,\n 42.87596410238256\n ],\n [\n -95.09765625,\n 45.644768217751924\n ],\n [\n -95.97656249999999,\n 49.03786794532644\n ],\n [\n -98.7890625,\n 50.45750402042058\n ],\n [\n -112.236328125,\n 52.10650519075632\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Northern Prairie Wildlife Research Center
U.S. Geological Survey
8711 37th Street Southeast
Jamestown, ND 58401
This report summarizes data-collection activities associated with the U.S. Geological Survey Humboldt Bay Water-Quality and Salt Marsh Monitoring Project. This work was undertaken to gain a comprehensive understanding of water-quality conditions, salt marsh accretion processes, marsh-edge erosion, and soil-carbon storage in Humboldt Bay, California. Multiparameter sondes recorded water temperature, specific conductance, and turbidity at a 15-minute timestep at two U.S. Geological Survey water-quality stations: (1) Mad River Slough near Arcata, California (U.S. Geological Survey station 405219124085601) and (2) Hookton Slough near Loleta, California (U.S. Geological Survey station 404038124131801). At each station, discrete water samples were collected to develop surrogate regression models that were used to compute a continuous time series of suspended-sediment concentration from continuously measured turbidity. Data loggers recorded water depth at a 6-minute timestep in the primary tidal channels (Mad River Slough and Hookton Slough) in two adjacent marshes (Mad River marsh and Hookton marsh). The marsh monitoring network included five study marshes. Three marshes (Mad River, Manila, and Jacoby) are in the northern embayment of Humboldt Bay and two marshes (White and Hookton) are in the southern embayment. Surface deposition and elevation change were measured using deep rod surface elevation tables and feldspar marker horizons. Sediment characteristics and soil-carbon storage were measured using a total of 10 shallow cores, distributed across 5 study marshes, collected using an Eijkelkamp peat sampler. Rates of marsh edge erosion (2010–19) were quantified in four marshes (Mad River, Manila, Jacoby, and White) by estimating changes in the areal extent of the vegetated marsh plain using repeat aerial imagery and light detection and ranging (LiDAR)-derived elevation data. During the monitoring period (2016–19), the mean suspended-sediment concentration computed for Hookton Slough (50±20 milligrams per liter [mg/L]) was higher than Mad River Slough (18±7 mg/L). Uncertainty in mean suspended-sediment concentration values is reported using a 90-percent confidence interval. Across the five study marshes, elevation change (+1.8±0.6 millimeters per year [mm/yr]) and surface deposition (+2.5±0.5 mm/yr) were lower than published values of local sea-level rise (4.9±0.8 mm/yr), and mean carbon density was 0.029±0.005 grams of carbon per cubic centimeter. From 2010 to 2019, marsh edge erosion and soil carbon loss were greatest in low-elevation marshes with the marsh edge characterized by a gentle transition from mudflat to vegetated marsh (herein, ramped edge morphology) and larger wind-wave exposure. Jacoby Creek marsh experienced the greatest edge erosion. In total, marsh edge erosion was responsible for 62.3 metric tons of estuarine soil carbon storage loss across four study marshes. Salt marshes are an important component of coastal carbon, which is frequently referred to as “blue carbon.” The monitoring data presented in this report provide fundamental information needed to manage blue carbon stocks, assess marsh vulnerability, inform sea-level rise adaptation planning, and build coastal resiliency to climate change.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221076","collaboration":"Prepared in cooperation with the California State Coastal Conservancy, California Department of Fish and Wildlife, and U.S. Fish and Wildlife Service—Humboldt Bay National Wildlife Refuge","usgsCitation":"Curtis, J.A., Thorne, K.M., Freeman, C.M., Buffington, K.J., and Drexler, J.Z., 2022, A summary of water-quality and salt marsh monitoring, Humboldt Bay, California: U.S. Geological Survey Open-File Report 2022–1076, 30 p., https://doi.org/10.3133/ofr20221076.","productDescription":"Report: viii, 30 p.; 3 Data Releases","numberOfPages":"30","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-133425","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":501826,"rank":9,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113519.htm","linkFileType":{"id":5,"text":"html"}},{"id":406874,"rank":8,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20221076/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":406865,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9TVX0Z8","text":"Model archive summary for a suspended-sediment concentration surrogate regression model for station 405219124085601; Mad River Slough near Arcata, CA","description":"Curtis, J.A., 2021b, Model archive summary for a suspended-sediment concentration surrogate regression model for station 405219124085601; Mad River Slough near Arcata, CA: U.S. Geological Survey data release, https://doi.org/10.5066/P9TVX0Z8."},{"id":406863,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1076/images"},{"id":406864,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9RJTAIL","text":"Model archive summary for a suspended-sediment concentration surrogate regression model for station 404038124131801; Hookton Slough near Loleta, CA","description":"Curtis, J.A., 2021a, Model archive summary for a suspended-sediment concentration surrogate regression model for station 404038124131801; Hookton Slough near Loleta, CA: U.S. Geological Survey data release, https://doi.org/10.5066/P9RJTAIL."},{"id":406866,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9QLAL7B","text":"Salt marsh monitoring during water years 2013 to 2019, Humboldt Bay, CA—Water levels, surface deposition, elevation change, and soil carbon storage","description":"Curtis, J.A., Thorne, K.M., Freeman, C.M., Buffington, K.J., and Drexler, J.Z., 2022, Salt marsh monitoring during water years 2013 to 2019, Humboldt Bay, CA—Water levels, surface deposition, elevation change, and soil carbon storage: U.S. Geological Survey data release, https://doi.org/10.5066/P9QLAL7B."},{"id":406860,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1076/covrthb.jpg"},{"id":406861,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1076/ofr20221076.pdf","text":"Report","size":"12 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":406862,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1076/ofr20221076.xml"}],"country":"United States","state":"California","otherGeospatial":"Humboldt Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.35699462890624,\n 40.55972134684838\n ],\n [\n -124.0191650390625,\n 40.55972134684838\n ],\n [\n -124.0191650390625,\n 40.97678774053031\n ],\n [\n -124.35699462890624,\n 40.97678774053031\n ],\n [\n -124.35699462890624,\n 40.55972134684838\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
Preliminary models for gate operations at the new outlet control structure for Reelfoot Lake were developed by the U.S. Geological Survey, using calibrated ratings of the lift gates, to support the U.S. Fish and Wildlife Service in managing lake level. In 2018, the old structure at the outlet of Reelfoot Lake was buried and lake level control was transferred to a new structure. The transition from lake-level management of the old control structure to the new control structure was documented using historical lake level and discharge measurements and records of stop-log management from March 7, 2013, to August 12, 2018. Discharge into Running Reelfoot Bayou was determined using a standard stage-discharge rating curve. Discharge measured using an acoustic Doppler current profiler was used to calibrate gate-discharge equations for free and submerged orifice flow at the new structure.
Two lake operation models, one for the summer season and another for the winter season, are provided for the new structure based on data from this period. The summer operation model is based on operation of the gates once the lake level exceeds an elevation of 282.7 feet (ft) above the North American Vertical Datum of 1988 (NAVD 88). Free flow begins when lake level reaches 282.3 ft above NAVD 88 and becomes transitional once the lake level exceeds 282.8 ft above NAVD 88. Submerged flow begins once the lake level reaches 283 ft above NAVD 88 and the tail-water depth is above critical flow depth. The winter operation model is based on operation of the gates once the lake level exceeds 283.2 ft above NAVD 88. Submerged flow begins when the lake rises to an elevation of 283.5 ft above NAVD 88 and the tail-water depth is above critical flow depth.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/ofr20221073","collaboration":"Prepared in cooperation with the Tennessee Wildlife Resources Agency","usgsCitation":"Heal, E.N., Diehl, T.H., and Garrett, J.W., 2022, Preliminary models relating lake level gate operation and discharge at Reelfoot Lake in Tennessee and Kentucky: U.S. Geological Survey Open-File Report 2022–1073, 27 p., https://doi.org/10.3133/ofr20221073.","productDescription":"Report: vii, 27 p.; Data Release; Database","onlineOnly":"Y","ipdsId":"IP-103756","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":406684,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9GY1UF4","text":"USGS data release","linkHelpText":"Preliminary model data for lake level gate operation and discharge at Reelfoot Lake—Tennessee and Kentucky"},{"id":501823,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113520.htm","linkFileType":{"id":5,"text":"html"}},{"id":406683,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1073/ofr20221073.pdf","text":"Report","size":"2.57 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1073"},{"id":406682,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1073/coverthb.jpg"},{"id":406685,"rank":4,"type":{"id":9,"text":"Database"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS water data for the Nation—","linkHelpText":"U.S. Geological Survey National Water Information System database"},{"id":406844,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1073/images"},{"id":406845,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1073/ofr20221073.xml"},{"id":409059,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/ofr20221073/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2022-1073"}],"country":"United States","state":"Kentucky, Tennessee","otherGeospatial":"Reelfoot Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.46441650390625,\n 36.30350540784278\n ],\n [\n -89.25155639648438,\n 36.30350540784278\n ],\n [\n -89.25155639648438,\n 36.52453591500483\n ],\n [\n -89.46441650390625,\n 36.52453591500483\n ],\n [\n -89.46441650390625,\n 36.30350540784278\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
640 Grassmere Park, Suite 100
Nashville, TN 37211
Since 2008, large-scale restoration programs have been implemented along the Willamette River, Oregon, to address historical losses of floodplain habitats caused by dam construction, bank protection, large wood removal, land conversion, and other anthropogenic influences. The Willamette Focused Investment Partnership (WFIP) restoration initiative brings together more than 16 organizations to improve floodplain habitats on more than 35,000 hectares upstream from Willamette Falls with the overarching goal to expand and enhance native fish habitats through the following restoration activities implemented along the floodplains and off-channel areas of the Willamette River: (A) modify floodplain topography and human-made barriers to inundation; (B) enhance gravel pits; (C) remove revetments; (D) construct off-channel features; (E) increase and enhance floodplain forest vegetation; and (F) treat aquatic invasive plant species (AIS). The WFIP Effectiveness Monitoring Program was initiated to inform future refinement of Willamette River restoration program goals and activities and has three goals: (1) evaluate the effectiveness of different restoration activities at increasing and enhancing native fish habitat, (2) improve overall understanding of the physical and ecological responses associated with different restoration activities undertaken by the WFIP, and (3) relate site-scale responses to restoration with broader patterns of fish communities, hydrogeomorphology, stream temperature, and vegetation across the Willamette River floodplain, so that the relative importance of restoration activities on habitat availability for native fish can be assessed.
A monitoring framework was developed to evaluate effectiveness of floodplain restoration activities at increasing and enhancing habitat for native fish in the Willamette River corridor, northwestern Oregon. This framework describes monitoring indicators, metrics, and approaches for evaluating responses in native fish communities and physical habitat conditions to restoration activities and determining effectiveness of restoration activities at improving habitats for native fish. The monitoring indicators and approaches are grouped into five restoration monitoring categories that are useful for characterizing ecological and physical habitat responses to restoration activities: fish, hydrogeomorphology, floodplain forest vegetation, birds, and AIS. This monitoring framework provides a common science foundation to support collaborative decisions on future interdisciplinary effectiveness monitoring activities for Willamette River restoration programs. To evaluate restoration effectiveness, data must be evaluated according to metrics and thresholds that permit direct comparison between habitat conditions at the restoration site and restoration program goals; this framework provides examples of metrics and thresholds for evaluating data, recognizing that the precise evaluation criteria for a particular site or program will need to be tailored to meet program questions and available resources. Refining restoration goals and activities as part of an adaptively managed process requires addressing critical uncertainties between restoration goals, restoration activities, and outcomes for habitats used by native fish. Although the monitoring activities of this framework will generate important datasets useful for evaluating restoration effectiveness, additional research, syntheses, and reporting is ultimately necessary to provide a common science foundation to support adaptively managed restoration programs. This report is intended as a resource for restoration program managers, practitioners, scientists, and contractors as they develop detailed annual monitoring plans for data collection and identify the monitoring indicators, metrics, and approaches that are appropriate for evaluating effectiveness of different restoration activities.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221037","collaboration":"Prepared in cooperation with Benton Soil and Water Conservation District and Oregon Watershed Enhancement Board","usgsCitation":"Keith, M.K., Wallick, J.R., Flitcroft, R.L., Kock, T.J., Brown, L.A., Miller, R., Hagar, J.C., Guillozet, K., and Jones, K.L., 2022, Monitoring framework to evaluate effectiveness of aquatic and floodplain habitat restoration activities for native fish along the Willamette River, northwestern Oregon: U.S. Geological Survey Open-File Report 2022–1037, 116 p., https://doi.org/10.3133/ofr20221037.","productDescription":"Report: xi,116 p.; Data Reease","onlineOnly":"Y","ipdsId":"IP-117547","costCenters":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":501771,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113503.htm","linkFileType":{"id":5,"text":"html"}},{"id":405744,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9N55MYW","text":"USGS data release","description":"USGS data release","linkHelpText":"Native and non-native fish species in the Willamette River Basin, Oregon"},{"id":405742,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1037/coverthb.jpg"},{"id":405743,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1037/ofr20221037.pdf","text":"Report","size":"77.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1037"}],"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 -123.42041015624999,\n 43.96119063892024\n ],\n [\n -122.36572265625,\n 43.96119063892024\n ],\n [\n -122.36572265625,\n 45.537136680398596\n ],\n [\n -123.42041015624999,\n 45.537136680398596\n ],\n [\n -123.42041015624999,\n 43.96119063892024\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Oregon Water Science Center
U.S. Geological Survey
2130 SW 5th Avenue
Portland, Oregon 97201
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 1 (January–March), 2022. 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/ofr20221072","usgsCitation":"Haque, M.O., Rengarajan, R., Lubke, M., Hasan, M.N., Tuli, F.T.Z., Shaw, J.L., Denevan, A., Franks, S., Micijevic, E., Choate, M.J., Anderson, C., Markham, B., Thome, K., Kaita, E., Barsi, J., Levy, R., and Ong, L., 2022, ECCOE Landsat Quarterly Calibration and Validation report—Quarter 1, 2022: U.S. Geological Survey Open-File Report 2022–1072, 39 p., https://doi.org/10.3133/ofr20221072.","productDescription":"Report: vii, 39 p.; Dataset","numberOfPages":"52","onlineOnly":"Y","ipdsId":"IP-140787","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":405985,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20221072/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":405885,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://earthexplorer.usgs.gov/","text":"USGS database","linkHelpText":"—EarthExplorer"},{"id":405884,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1072/images"},{"id":405883,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1072/ofr20221072.XML"},{"id":405880,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1072/coverthb.jpg"},{"id":405882,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1072/ofr20221072.pdf","text":"Report","size":"4.31 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022–1072"}],"contact":"Director, Earth Resources Observation and Science Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
The Deepwater Horizon oil spill caused extensive injury to natural resources in the Gulf of Mexico, and Gavia immer (common loon) were negatively affected from the spill. The Open Ocean Trustee Implementation Group funded the project Restoration of Common Loons in Minnesota to restore common loons lost to the spill. In 2020–21, priority lakes in an eight-county region in north-central Minnesota were identified to focus project activities. In 2021, surveys on these lakes were started to monitor common loon territory occupancy, nest success, and chick survival. We surveyed 62 lakes and identified 110 common loon territories that will be included in the project. At least 1 nest attempt was observed in 78 of 110 territories, and a second nest attempt was observed in 23 territories. A third nest attempt was observed in one territory. Successful nesting was observed in 32 of 110 territories. We present no formal data analysis and plan to analyze the data after the collection of all field data in subsequent years.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221074","usgsCitation":"Beatty, W.S., Fara, L.J., Houdek, S.C., Kenow, K.P., and Gray, B.R., 2022, Restoration of Gavia immer (common loon) in Minnesota—2021 annual report: U.S. Geological Survey Open-File Report 2022–1074, 7 p., https://doi.org/10.3133/ofr20221074.","productDescription":"vi, 7 p.","numberOfPages":"16","onlineOnly":"Y","ipdsId":"IP-139232","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":435709,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9LA536E","text":"USGS data release","linkHelpText":"Summary of Detection Data for Breeding Common Loons in North-central Minnesota (2021-2022) (ver. 1.1, August 2024)"},{"id":405938,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20221074/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":405361,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1074/ofr20221074.XML"},{"id":405360,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1074/ofr20221074.pdf","text":"Report","size":"2.34 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022–1074"},{"id":405359,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1074/coverthb.jpg"},{"id":405362,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1074/images"}],"country":"United States","state":"Minnesota","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -96.78955078125,\n 46.27103747280261\n ],\n [\n -93.240966796875,\n 46.27103747280261\n ],\n [\n -93.240966796875,\n 48.821332549646634\n ],\n [\n -96.78955078125,\n 48.821332549646634\n ],\n [\n -96.78955078125,\n 46.27103747280261\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Upper Midwest Environmental Sciences Center
U.S. Geological Survey
2630 Fanta Reed Road
La Crosse, WI 54603
Surveys and monitoring for the coastal Cactus Wren (Campylorhynchus brunneicapillus) were completed in San Diego County between March 2015 and July 2019. A total of 383 plots were surveyed across 3 genetic clusters (Otay, Lake Jennings, and Sweetwater/Encanto). From 2015 to 2019, 317 plots were surveyed 8 times (twice per year in 2015, 2017–19). Additional plots were added in later years as wrens were discovered in new locations. We found differences in the proportion of plots occupied in the genetic clusters, with a lower proportion of plots occupied in the Otay cluster than in the Lake Jennings and Sweetwater/Encanto clusters in all years. Plot occupancy increased each year in the Otay and Sweetwater/Encanto clusters but not in the Lake Jennings cluster. The number of Cactus Wren territories increased from 2015 through 2018, and then decreased in 2019 in all three genetic clusters.
We monitored nesting activities for two populations of Cactus Wrens in southern San Diego County. The Otay population consisted of two sites within the Otay genetic cluster, and the San Diego population consisted of two sites within the Sweetwater/Encanto and Lake Jennings genetic clusters. Nest monitoring occurred at 10–13 territories per year in the Otay population and 14–18 territories in the San Diego population from 2015 through 2019. All territories were occupied by pairs except two territories in 2015, five in 2016, and two in 2019. Between 46 and 74 Cactus Wren nests were monitored each year, which totaled 295 monitored nests from 2015 to 2019. To evaluate the direct influence of precipitation on breeding success, bio-year precipitation (“precipitation”) was calculated from July 1 of the prior year through June 30 of the breeding season year. Overall apparent nest success was positively influenced by precipitation with the lowest apparent nest success of 50 percent in 2015 and the highest apparent nest success of 72 percent in 2017, corresponding to the second lowest and the highest precipitation years, respectively. Apparent nest success also was higher in the Otay population than in the San Diego population. The number of brood nests initiated per pair and the number of renesting attempts per pair also were higher in years with more precipitation. Other metrics of Cactus Wren nesting success and productivity were positively influenced by the amount of precipitation, including clutch size and egg hatching success. The percent of hatchlings that fledged was greater in the Otay population than in the San Diego population but was not influenced by precipitation. The number of fledglings per pair was higher in years with more precipitation and was greater in the Otay population than in the San Diego population. Predation was the predominant cause of nest failure in both populations.
Analysis of Cactus Wren daily nest survival rate indicated that there was a population, and possibly a precipitation effect on nest survival, with the daily survival rate for the Otay population significantly higher than for the San Diego population and weak increase in the daily survival rate with more precipitation.
A total of 629 Cactus Wrens were banded during the course of the study, 360 in the San Diego population and 269 in the Otay population. Between 2015 and 2019, we resighted 301 color-banded adult birds that ranged between 1 and 8 years old. One additional color-banded bird was resighted in San Pasqual Valley (as part of a separate study); this bird originated in the San Diego population and was excluded from our analyses.
Annual survival was higher for adult Cactus Wrens (ranging from 60 to 70 percent) than for first-year wrens (ranging from 20 to 28 percent) and varied by year. Annual survival was also weakly but positively correlated with precipitation. Annual survival was higher for first year and adult Cactus Wrens following years with increased precipitation. We found no evidence that survival differed by population.
Banding also allowed us to examine whether there were differences in movement of adult and first-year Cactus Wrens by year or by population. We found that average dispersal distance for first-year Cactus Wrens was 1.9 kilometers in the Otay population and 1.6 kilometers in the San Diego population and did not differ by population or year. Dispersal between populations was not common. We detected five instances of movement of first-year wrens between the San Diego and Otay populations. All movements into and out of the San Diego population were from or into territories in the Sweetwater area. We detected no movement between the Lake Jennings site and either of the Sweetwater or Otay sites; however, we did detect one wren that dispersed from Lake Jennings to the San Pasqual Valley population in 2019, which was a distance of 26.4 kilometers. Adult Cactus Wrens were site-faithful, with 87 percent of adults remaining on the same territory between breeding seasons. Precipitation may be a weak driver of movement for adult Cactus Wrens, with adults more likely to remain on the same territory following years of increased precipitation. There was no difference in adult movement between populations.
Arthropods were collected in pitfall traps and by vacuum in 23 Cactus Wren territories during 3 sampling periods in 2016 (early nesting, peak nesting, and late nesting). Arthropods of 19 orders and at least 128 families were collected. Analysis of 43 Cactus Wren fecal samples identified 10 arthropod orders that were present in more than 10 percent of fecal samples. The most abundant arthropod order collected was Hymenoptera; however, Cactus Wrens consumed arthropods in the order Hymenoptera significantly less than their availability, suggesting that this order was avoided. No other orders were significantly selected or avoided; however, selection indices of arthropod families identified that two families of arthropods (Isopoda Porcellionidae [woodlice] and Hymenoptera Formicidae [ants]) were avoided. After excluding the taxa that were avoided or not represented in fecal samples, 95 percent of Cactus Wren prey items were collected in pitfall traps and 5 percent were collected by vacuum. The most abundant prey orders captured were Diptera, Coleoptera, Hemiptera, Hymenoptera, and Aranea.
Analysis of the abundance of Cactus Wren prey items by vegetation type and sampling period indicated that vegetation type by itself was not a significant predictor of arthropod abundance but interacted with sampling period. Seasonal availability of arthropods was highest in the peak nesting period, followed by early and late nesting periods for California sagebrush (Artemisia californica), lemonadeberry (Rhus integrifolia), non-native grass, and bare ground, whereas availability increased from early to late nesting periods for blue elderberry (Sambucus mexicana spp. caerulea), cactus (Opuntia spp. and Cylindropuntia spp.), California buckwheat (Eriogonum fasciculatum), native bunch grasses, and black mustard (Brassica nigra). During the early nesting period, arthropods were most abundant in native bunch grasses and least abundant in lemonadeberry. During the peak nesting period, arthropods were most abundant in native bunch grasses and in areas of bare ground and were least abundant in cactus and blue elderberry. During late nesting, arthropods were most abundant in blue elderberry and non-native grass and least abundant in lemonadeberry and mustard.
Each year from 2015 to 2019, vegetation data were collected at the same 23 territories where arthropods were sampled: 9 territories in the Otay population and 14 territories in the San Diego population. Cactus, California buckwheat, and non-native grasses were detected within at least 60 percent of sampling points in the Otay population. Cactus, California sagebrush, California buckwheat, non-native grass, and black mustard each were detected within an average of 40 percent of sampling points in the San Diego population. No native bunch grass or lemonadeberry were recorded at the Lake Jennings site within the San Diego population. The cover of shrub species was relatively stable throughout the 5 years. Cover of herbaceous species and bare ground had greater annual variation than shrub species.
We found that vegetation cover varied widely among territories, with territory accounting for 69 percent of the variation in vegetation cover. Redundancy analysis allowed us to identify the vegetation types that accounted for the most variation. We used the top scores from the redundancy analysis to identify six vegetation types to be used in generalized linear mixed models analyzing the relationships between vegetation type, precipitation, and Cactus Wren breeding productivity. Three vegetation variables influenced the number of fledglings produced per pair. California sagebrush had a positive effect on the number of fledglings per pair whereas non-native grass and black mustard had a negative effect.
Breeding productivity, survival, and movements of adult and first-year Cactus Wrens indicated that the Otay population behaved similarly to, if not out-performed, the San Diego population during the span of our project, suggesting that the driving forces behind low numbers of Cactus Wrens in the Otay population before 2015 were no longer in effect. The Cactus Wren populations in Otay and San Diego reached a peak in 2018, which followed a year of high productivity and survivorship, both of which were correlated with high precipitation. This peak in population size was consistent with reproductive timing and productivity in other bird populations in semi-arid ecosystems that were linked to precipitation and arthropod abundance. We did not find a strong link among arthropod abundance, vegetation composition, and Cactus Wren breeding productivity, likely in part because arthropod abundance varied by vegetation type and sampling period, suggesting that different vegetation types provided important sources of prey at different periods of the breeding season. Arthropod abundance also may not represent arthropod availability when vegetation structure discourages the ground foraging behavior of species such as Cactus Wrens. Cover of non-native grass negatively influenced breeding productivity, although arthropods were abundant in non-native grass. Other factors that could have influenced differential breeding productivity between the Otay and San Diego populations were habitat restoration, control of annual herbaceous vegetation, human disturbance, lingering effects of wildfire, and nest predation. Overall, precipitation appeared to be a driver of Cactus Wren breeding productivity and possibly survival, potentially obscuring proximate effects of arthropod or vegetation composition.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221044","programNote":"Ecosystems Mission Area—Species Management Research Program","usgsCitation":"Lynn, S., Houston, A., and Kus, B.E., 2022, Distribution and demography of Coastal Cactus Wrens in Southern California, 2015–19: U.S. Geological Survey Open-File Report 2022-1044, 44 p., https://doi.org/10.3133/ofr20221044.","productDescription":"Report: ix, 44 p.; Data Release","numberOfPages":"44","onlineOnly":"Y","ipdsId":"IP-136839","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":435711,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P143ZTB2","text":"USGS data release","linkHelpText":"Cactus Wren Invertebrate Diet Derived from Sequencing of Nestling Fecal Samples in San Diego County, California"},{"id":405951,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20221044/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"Open-File Report 2022-1044"},{"id":405841,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1044/images"},{"id":405840,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1044/ofr20221044.xml"},{"id":405839,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1044/ofr20221044.pdf","text":"Report","size":"4 MB"},{"id":405838,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1044/covrthb.jpg"},{"id":405842,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F76H4FK5","text":"Surveys and Monitoring of Coastal Cactus Wren in Southern San Diego County","description":"Kus, B.E., and Lynn, S., 2022, Surveys and monitoring of Coastal Cactus Wren in southern San Diego County: U.S. Geological Survey data release, https://doi.org/10.5066/F76H4FK5."}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.3065185546875,\n 32.52365781569917\n ],\n [\n -116.630859375,\n 32.52365781569917\n ],\n [\n -116.630859375,\n 32.983324091837474\n ],\n [\n -117.3065185546875,\n 32.983324091837474\n ],\n [\n -117.3065185546875,\n 32.52365781569917\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
Pests and invasive species have been defined as any organism that can have real or perceived adverse effects on operations, or the well-being of personnel, native plants, animals, their environment and ecosystem processes; attack or damage real property, supplies, equipment, or are otherwise undesirable (paraphrased from many sources including 53 Federal Register [FR] 15975, May 4, 1988, as amended at 78 FR 13507, February 28, 2013). Biosecurity programs and pest management plans can be developed and implemented with the goals of preventing the arrival of or eradication or control of pests and invasive species to reduce the potential for adverse effects. Such plans have been developed for Wake Atoll (U.S. Air Force, unpub. data 2017). Periodic plan reviews are an integral step for evaluating plan efficacy and updating plans with new information for improving plan effectiveness. This report summarizes an evaluation of past, current, and potential biosecurity and pest management for Wake with the intent this information can be used for updating existing plans. This document was prepared in cooperation with the U.S. Air Force (USAF) and surveys were performed for the 611th Civil Engineer Squadron Natural Resources Program ACES PROJECT no. YGFZ17002 under agreement number F2MUAA7116GW01 between the USAF and the U.S. Geological Survey’s Western Ecological Research Center (USGS-WERC).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221067","collaboration":"Prepared in cooperation with the U.S. Air Force","programNote":"Ecosystems Mission Area—Land Management Research and Species Management Research Programs","usgsCitation":"Hathaway, S.A., Jacobi, J.D., Peck, R., and Fisher, R.N., 2022, Updates for Wake Atoll biosecurity management, biological control, survey, and management, and integrated pest management plans: U.S. Geological Survey Open-File Report 2022-1067, 56 p., https://doi.org/10.3133/ofr20221067.","productDescription":"viii, 56 p.","numberOfPages":"56","onlineOnly":"Y","ipdsId":"IP-136103","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true},{"id":5049,"text":"Pacific Islands Ecosys Research Center","active":true,"usgs":true}],"links":[{"id":405629,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1067/ofr20221067.xml"},{"id":405628,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1067/ofr20221067.pdf","text":"Report","size":"10 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":405627,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1067/covrthb.jpg"},{"id":501820,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113419.htm","linkFileType":{"id":5,"text":"html"}},{"id":405630,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1067/images"}],"contact":"Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
This report addresses system characterization of the Instituto Nacional de Pesquisas Espaciais Amazônia-1 satellite 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.
Amazônia-1 is a four-band imager with a 64-meter (m) pixel ground sample distance. Amazônia-1 was launched in February 2021 into a Sun-synchronous orbit of 752 kilometers with an inclination of 98.4 degrees and a swath width of 850 kilometers. The satellite has an expected lifetime of about 4 years. More information on Amazônia-1 is available in the “Land Remote Sensing Satellites Online Compendium” (https://calval.cr.usgs.gov/apps/compendium).
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 Amazônia-1 satellite has an interior geometric performance in the range of −3.584 m (−0.056 pixel) to 0.320 m (0.005 pixel) in easting and −1.984 m (−0.031 pixel) to 2.048 m (0.032 pixel) in northing in band-to-band registration, an exterior geometric performance of −37.256 m (−0.621 pixel) to 54.758 m (0.913 pixel) in easting and −12.684 m (−0.211 pixel) to 54.898 m (0.915 pixel) in northing offset in comparison to the Landsat 8 Operational Land Imager, a radiometric performance in the range of 0.030 to 0.143 in offset and 0.662 to 0.825 in slope, and a spatial performance in the range of 1.62 to 2.06 pixels for full width at half maximum, with a modulation transfer function at a Nyquist frequency in the range of 0.062 to 0.115.
","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/ofr20211030N","usgsCitation":"Vrabel, J.C., Stensaas, G.L., Anderson, C., Christopherson, J., Kim, M., and Park, S., 2022, System characterization report on the Amazônia-1 multispectral sensor, chap. N of Ramaseri Chandra, S.N., comp., System characterization of Earth observation sensors: U.S. Geological Survey Open-File Report 2021–1030, 33 p., https://doi.org/10.3133/ofr20211030N.","productDescription":"v, 33 p.","numberOfPages":"44","onlineOnly":"Y","ipdsId":"IP-142103","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":405398,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1030/n/ofr20211030n.pdf","text":"Report","size":"2.29 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021–1030–N"},{"id":405397,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1030/n/coverthb.jpg"}],"contact":"Director, Earth Resources Observation and Science (EROS) Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
Land subsidence is a settling, sinking, or collapse of the land surface. In the southeastern United States, subsidence is frequently observed as sinkhole collapse in karst environments, wetland degradation and loss in coastal and other low-lying areas, and inundation of coastal urban communities. Human activities such as fluid extraction, mining, and overburden alteration can cause or exacerbate subsidence, which can result in damage to infrastructure and resources. Subsidence is a hazard that takes place throughout the United States; however, a systematic approach to recognize and develop informed responses to the drivers of subsidence has not yet been fully established. To address this problem, the U.S. Geological Survey (USGS) Southeast Region (SER) funded the gathering of a team of interdisciplinary USGS scientists to promote scientific collaboration. Southeast Region scientists welcomed scientists from other regions (see table 1.1 in Appendix 1) in September 2018 at the St. Petersburg Coastal and Marine Science Center (SPCMSC) in Florida for the first workshop of the Subsidence Flex Team (SFT) (see Appendix 2 for agenda). The SFT set out to review subsidence-related research and technology and develop a unifying framework for describing the processes and hazards associated with land subsidence. A more comprehensive understanding of subsidence hazards could help to inform regional vulnerability assessments that would prove invaluable to the public, community developers, policy makers, and resource managers in both inland and coastal states. The SFT analyzed USGS strengths and weaknesses to identify existing infrastructure and capabilities that could be leveraged to create a comprehensive and far-reaching subsidence-monitoring and mitigation program. Over the course of the 2-day workshop, interdisciplinary understandings of the processes and hazards related to subsidence were explored through individual presentations and group discussion. With all perspectives considered, the SFT recommended that subsidence-related research develop scientific approaches and metrics by which the subsidence component can be isolated and quantified in order to protect both the environment and human infrastructure from harm.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221064","usgsCitation":"Flocks, J., McGraw, E., Barras, J., Bernier, J., Bradley, M., Galloway, D., Landmeyer, J., McBride, W.S., Smith, C., Smith, K., Swarzenski, C., and Toth, L., 2022, Documenting the multiple facets of a subsiding landscape from coastal cities and wetlands to the continental shelf: U.S. Geological Survey Open-File Report 2022–1064, 22 p., https://doi.org/10.3133/ofr20221064.","productDescription":"viii, 22 p.","numberOfPages":"22","onlineOnly":"Y","ipdsId":"IP-106940","costCenters":[{"id":269,"text":"FLWSC-Ft. Lauderdale","active":true,"usgs":true},{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true},{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true},{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":501871,"rank":3,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113357.htm","linkFileType":{"id":5,"text":"html"}},{"id":404582,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1064/covrthb.jpg"},{"id":404583,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1064/ofr20221064.pdf","text":"Report","size":"10 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1064"}],"country":"United States","state":"Alabama, Florida, Louisiana, Mississippi","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.603515625,\n 24.5271348225978\n ],\n [\n -79.7607421875,\n 24.5271348225978\n ],\n [\n -79.7607421875,\n 31.16580958786196\n ],\n [\n -93.603515625,\n 31.16580958786196\n ],\n [\n -93.603515625,\n 24.5271348225978\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
The locations of many faults in and near the Rialto-Colton groundwater subbasin are not precisely known because the spatial density of existing lithologic and hydrologic data used to infer the locations of faults can be sparse. The U.S. Geological Survey, in cooperation with the San Bernardino Valley Municipal Water District, analyzed structural control of groundwater flow in and near the Rialto-Colton groundwater subbasin using Interferometric Synthetic Aperture Radar (InSAR) methods. Faults commonly are barriers to groundwater flow, and the high spatial resolution of InSAR imagery can be used to infer the locations of buried faults where groundwater pumping occurs. InSAR results have revealed three areas in and near the Rialto-Colton groundwater subbasin where buried faults are interpreted as groundwater-flow barriers: the northwestern area about 3 miles northwest of the City of Rialto, the San Jacinto fault area west of the City of San Bernardino, and the southeastern area about 2 miles southeast of the City of Colton. The InSAR results were combined with knowledge gained from previous studies to better define the location and extent of faults acting as groundwater-flow barriers. New data about faults acting as groundwater-flow barriers can be incorporated into future conceptual and hydrologic models of the Rialto-Colton groundwater subbasin and provide water managers information to help effectively manage groundwater resources.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221030","collaboration":"Prepared in cooperation with the San Bernardino Valley Municipal Water District","programNote":"Water Availability and Use Science Program","usgsCitation":"Brandt, J.T., 2022, Mapping structural control through analysis of land-surface deformation for the Rialto-Colton groundwater subbasin, San Bernardino County, California, 1992–2010: U.S. Geological Survey Open-File Report 2022–1030, 11 p., https://doi.org/10.3133/ofr20221030.","productDescription":"Report: vi, 11 p.; Data Release","numberOfPages":"11","onlineOnly":"Y","ipdsId":"IP-084965","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":501769,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113347.htm","linkFileType":{"id":5,"text":"html"}},{"id":403230,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1030/images"},{"id":403228,"rank":1,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1030/ofr20221030.xml"},{"id":403229,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1030/ofr20221030.pdf","text":"Report","size":"3 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":403232,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7P55KJN","text":"Data release","description":"U.S. Geological Survey, 2014, Web interface: U.S. Geological Survey National Water Information System web page, accessed June 11, 2014, at https://doi.org/10.5066/F7P55KJN.","linkHelpText":"Web interface: U.S. Geological Survey National Water Information System web page"},{"id":404520,"rank":5,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1030/covrthb.jpg"},{"id":404546,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20221030/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2022-1030"}],"country":"United States","state":"California","county":"San Bernardino County","otherGeospatial":"Rialto-Colton groundwater subbasin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.51319885253905,\n 34.01851844336969\n ],\n [\n -117.2138214111328,\n 34.01851844336969\n ],\n [\n -117.2138214111328,\n 34.19362958613085\n ],\n [\n -117.51319885253905,\n 34.19362958613085\n ],\n [\n -117.51319885253905,\n 34.01851844336969\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
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In light of ongoing and geographically widespread highly pathogenic avian influenza (HPAI) outbreaks in wild birds throughout much of Eurasia during 2020–21, the Interagency Steering Committee for Avian Influenza Surveillance in Wild Migratory Birds disseminated an informational memorandum in January 2021 to highlight the need for enhanced surveillance and heightened awareness in North America. This was followed by coordination of this August 2021 international HPAI webinar series facilitated by the U.S. Department of Agriculture (USDA) Animal and Plant Health Inspection Service (APHIS) Veterinary Services Training Program. In addition to heightening awareness, the webinars provided an opportunity for information exchange and facilitated virtual discussions between Federal, State, academic, and international partners on the ongoing Eurasian outbreak, lessons learned from the 2014–15 North American HPAI outbreak, and associated challenges and opportunities.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221066","usgsCitation":"Hopkins, M.C., Mason, J.R., Haddock, G., and Ramey, A.M., 2022, Proceedings of the Highly Pathogenic Avian Influenza and Wild Birds Webinar Series, August 2–5, 2021: U.S. Geological Survey Open-File Report 2022–1066, 27 p., https://doi.org/10.3133/ofr20221066.","productDescription":"vi, 27 p.","numberOfPages":"23","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-140410","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":506,"text":"Office of the AD Ecosystems","active":true,"usgs":true}],"links":[{"id":404536,"rank":4,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20221066/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2022-1066"},{"id":403060,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1066/images/"},{"id":403057,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1066/coverthb.jpg"},{"id":403058,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1066/ofr20221066.pdf","text":"Report","size":"2.31 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1066"},{"id":403059,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1066/ofr20221066.XML"}],"contact":"Associate Director, Ecosystems
U.S. Geological Survey
954 National Center
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Reston, VA 20192
A reported fuel release in November 2021 at the Red Hill Bulk Fuel Storage Facility within the naval reservation at Red Hill led to the shutdown of several production wells in the Hālawa area, O‘ahu, Hawai‘i. Red Hill Shaft—one of the high-capacity production wells that shut down—was reactivated on January 29, 2022. Submersible pressure transducers were deployed at 20 wells in the Hālawa area to measure groundwater levels and evaluate the regional groundwater-level response to the resumption of groundwater withdrawals from Red Hill Shaft. Groundwater levels measured in wells from January 17 to March 3, 2022, ranged between 16 and 20 feet at all sites and generally between 17 and 19 feet at most sites. Average groundwater-level decreases measured in wells 10 days after the January 29, 2022, resumption of withdrawal from Red Hill Shaft ranged from about 0.1 to 0.4 foot. In general, greatest decreases in groundwater levels occurred in wells closest to Red Hill Shaft.
The groundwater-level data contain uncertainty because of several potential sources of error associated with (1) the accuracy of the measuring tapes and submersible pressure transducers used, (2) the accuracy of the measuring-point altitude at the top of each well, (3) the stability of the submersible pressure transducers’ suspension depth in each well, (4) well plumbness and alignment, and (5) human error. Because of the potential sources of error, comparability of groundwater-level data may be affected. Some sources of uncertainty, including the accuracy of measuring-point altitudes, can be addressed and lead to improved accuracy and comparability of groundwater levels. Data collected for this study are available in the U.S. Geological Survey National Water Information System database.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221069","collaboration":"Prepared in cooperation with the U.S. Navy","usgsCitation":"Nakama, R.K., Mitchell, J.N., and Oki, D.S., 2022, Groundwater-level monitoring from January 17 to March 3, 2022, Hālawa area, O‘ahu, Hawai‘i: U.S. Geological Survey Open-File Report 2022–1069, 29 p., https://doi.org/10.3133/ofr20221069.","productDescription":"Report: vi, 29 p.; Data Release","numberOfPages":"29","onlineOnly":"Y","ipdsId":"IP-141201","costCenters":[{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true}],"links":[{"id":501822,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113349.htm","linkFileType":{"id":5,"text":"html"}},{"id":404436,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20221018","text":"Open-File Report 2022-1018","description":"Nakama, R.K., Mitchell, J.N., and Oki, D.S., 2022, December 23, 2021, Red Hill synoptic groundwater-level survey, Hālawa area, O‘ahu, Hawai‘i: U.S. Geological Survey Open-File Report 2022–1018, 10 p., https://doi.org/10.3133/ofr20221018.","linkHelpText":"- December 23, 2021, Red Hill Synoptic Groundwater-Level Survey, Hālawa Area, O‘ahu, Hawai‘i"},{"id":404435,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20221048","text":"Open-File Report 2022-1048","description":"Nakama, R.K., Mitchell, J.N., and Oki, D.S., 2022, January 18, 2022, Red Hill synoptic groundwater-level survey, Hālawa area, O‘ahu, Hawai‘i: U.S. Geological Survey Open-File Report 2022–1048, 11 p., https://doi.org/10.3133/ofr20221048.","linkHelpText":"- January 18, 2022, Red Hill Synoptic Groundwater-Level Survey, Hālawa Area, O‘ahu, Hawai‘i"},{"id":404434,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS water data for the nation","description":"U.S. Geological Survey, 2022, USGS water data for the nation: U.S. Geological Survey National Water Information System database, https://doi.org/10.5066/F7P55KJN."},{"id":404433,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1069/ofr20221069.pdf","text":"Report","size":"7 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":404432,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1069/covrthb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Hālawa Area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -157.96142578124997,\n 21.317522325157526\n ],\n [\n -157.85156249999997,\n 21.317522325157526\n ],\n [\n -157.85156249999997,\n 21.409605198965597\n ],\n [\n -157.96142578124997,\n 21.409605198965597\n ],\n [\n -157.96142578124997,\n 21.317522325157526\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
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The U.S. Geological Survey Rocky Mountain Region hosted scientists, managers, program coordinators, and leadership team members for a virtual Science Exchange during September 15–17, 2020. The Science Exchange had 216 registered participants and included 48 talks over the 3-day period. Invited speakers presented information about the novel U.S. Geological Survey Earth Monitoring, Analysis, and Prediction (EarthMAP) concept. Scientists showcased their research and participated in discussions related to the EarthMAP concept and EarthMAP applications. In addition, the Colorado River Basin Pilot Project, one of the first EarthMAP Pilot Projects, was unveiled during the Science Exchange. This report provides synopses of session objectives and corresponding abstracts that were presented along with author affiliations and email address of the lead author. In addition, web links are provided for related programs and projects, and associated publications are referenced.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/ofr20221040","usgsCitation":"Anderson, P.J., and Tillery, A.C., 2022, Presented abstracts from the U.S. Geological Survey 2020 Rocky Mountain Region Science Exchange (September 15–17, 2020): U.S. Geological Survey Open-File Report 2022–1040, 23 p., https://doi.org/10.3133/ofr20221040.","productDescription":"x, 23 p.","onlineOnly":"Y","ipdsId":"IP-132301","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":405563,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20221040/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2022-1040"},{"id":403936,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1040/ofr20221040.xml"},{"id":403935,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1040/images"},{"id":403934,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1040/ofr20221040.pdf","text":"Report","size":"2.55 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1040"},{"id":403933,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1040/coverthb.jpg"}],"contact":"Director, Region 7 - Upper Colorado Basin
U.S. Geological Survey
P.O. Box 25046, Mail Stop 911
Denver, CO 80225
The Federal Interagency Sedimentation Project (FISP) standardizes and advances sediment science among federal agencies. It is important to ensure that the FISP bag samplers perform isokinetically under all tested and approved conditions and collect samples that are representative of the stream or river cross-section. A measure of a sampler’s isokinetic behavior is its intake efficiency, which is defined as the ratio of the velocity through the nozzle entrance of the sampler to the ambient stream velocity. The intake efficiencies of all FISP bag samplers and nozzle sizes were evaluated for this report. Samples were obtained across 31 U.S. Geological Survey streamflow-gaging stations between July 15, 2013, and June 17, 2020, where data were collected with all four bag samplers (US D-96, D-96-A1, D-99, and DH-2), each using various 3/16-inch, 1/4-inch, or 5/16-inch diameter nozzles.
Water temperature and ambient stream velocity outside the nozzle are two of several factors that are known to affect the intake efficiency of bag samplers. A regression curve was fitted to these data through LOWESS (locally weighted scatterplot smoothing), and a Kruskal-Wallis test was executed for the various samplers and nozzle sizes. Based on these results, there is no statistical evidence to indicate that water temperature and stream velocity have a noticeable effect on intake efficiency when the samplers are deployed under isokinetic conditions. Likewise, there is no statistical evidence to indicate that the type of bag sampler and nozzle diameter have a direct effect on intake efficiency.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221036","usgsCitation":"Manaster, A.E., Landers, M.N., and Straub, T.D., 2022, Intake efficiency field results for Federal Interagency Sedimentation Project bag samplers: U.S. Geological Survey Open-File Report 2022–1036, 27 p., https://doi.org/10.3133/ofr20221036.","productDescription":"Report: iv, 27 p.; Database","onlineOnly":"Y","ipdsId":"IP-134358","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":404073,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1036/coverthb.jpg"},{"id":404075,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1036/ofr20221036.pdf","text":"Report","size":"2.91 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 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U.S. Geological Survey
405 North Goodwin Ave.
Urbana, IL 61801
Concentrations of suspended sediment were measured in discrete samples and turbidity was continuously monitored at four U.S. Geological Survey streamgages in western Washington State, including one gage on the Sauk River; two gages on the Suiattle River, a tributary to the Sauk River; and one gage on Downey Creek, a tributary to the Suiattle River. The Suiattle River is a sediment-rich stream with headwaters on Glacier Peak, a glaciated volcano in the northern Cascade Range.
Contributions of suspended sediment to the Suiattle River from unglaciated tributaries, represented by Downey Creek, were compared to the contributions from Glacier Peak in the upper Suiattle River watershed. During summer 2017, a period for which complete records of discharge and sediment data were available for all three streamgages in the Suiattle River Basin, the suspended-sediment load from Downey Creek (drainage area [DA] 93 square kilometers [km2]) was 1,400 metric tons, which is equivalent to a sediment yield of about 15 metric tons per km2. During the same period, the suspended-sediment load from the upper Suiattle River (DA 176 km2) was 142,000 metric tons, or a sediment yield of about 800 metric tons per km2; and the suspended-sediment load from the lower Suiattle River (DA 733 km2) was 230,000 metric tons, or a sediment yield of about 300 metric tons per km2. The Downey Creek Basin accounts for 13 percent of the drainage area of the Suiattle River watershed but contributed only 0.6 percent of the suspended-sediment load over the summer of 2017 and water year 2017 (October 1, 2016–September 30, 2017). In contrast, the upper Suiattle River Basin, which accounts for 24 percent of the entire Suiattle River watershed, contributed 62 percent of the suspended-sediment load during the summer of 2017.
Given the short period for which data were collected, it cannot be known with certainty whether the above values are representative of long-term means. The relatively minor contribution of suspended sediment from Downey Creek, however, is consistent with the expectation that the upper Suiattle River, which drains Glacier Peak, is the dominant contemporary source of suspended sediment to the Sauk River. During summer 2016, the suspended-sediment load in the upper Siuattle River (180,000 metric tons) was more than double the estimated load in the lower Sauk River (80,000 metric tons), even though the upper Suiattle River represents only 10 percent of the total contributing area to the lower Sauk River Basin. This ratio of relative contribution is interpreted as an indication of transient storage of sediment along the Suiattle and Sauk Rivers between the two streamgaging stations. In the glaciated upper Suiattle River Basin, sediment is transported by annual glacial-melt processes in spring and summer months, deposited during the summer base-flow period, and then remobilized by fall and winter floods for delivery to the lower Sauk River.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221056","collaboration":"Prepared in cooperation with the Sauk-Suiattle Indian Tribe","usgsCitation":"Jaeger, K.L., Anderson, S.W., Senter, C.A., Curran, C.A., and Morris, S., 2022, Relative contributions of suspended sediment between the upper Suiattle River Basin and a non-glacial tributary, Washington, May 2016–September 2017: U.S. Geological Survey Open-File Report 2022–1056, 18 p., https://doi.org/10.3133/ofr20221056.","productDescription":"v, 18 p.","onlineOnly":"Y","ipdsId":"IP-132545","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":403971,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1056/ofr20221056.pdf","text":"Report","size":"8.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1056"},{"id":403970,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1056/coverthb.jpg"},{"id":501781,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113307.htm","linkFileType":{"id":5,"text":"html"}},{"id":403972,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/ofr20221056/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2022-1056"},{"id":404178,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9W74K0K","text":"USGS data release","description":"USGS data release","linkHelpText":"Suspended sediment and water temperature data in the Suiattle River and the Downey Creek Tributary, Washington for select time periods over 2013 - 2017 (ver. 2.0, October 2021)"},{"id":403974,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1056/ofr20221056.XML"},{"id":403973,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1056/images"}],"country":"United States","state":"Washington","otherGeospatial":"Upper Suiattle River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.4,\n 48.0\n ],\n [\n -121.0,\n 48.0\n ],\n [\n -121.0,\n 48.4\n ],\n [\n -121.4,\n 48.4\n ],\n [\n -121.4,\n 48.0\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Washington Water Science Center
U.S. Geological Survey
934 Broadway, Suite 300
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Numerous porphyry copper-molybdenum-gold and epithermal deposits define a belt that extends from Eastern Alaska to western Yukon, Canada. An orientation study conducted near the Taurus porphyry deposit was designed to test methods that require minimal sample collection, preparation, and analytical time to determine the viability of indicator mineral studies as a reconnaissance exploration method. Bulk stream sediments and altered and mineralized rocks were sieved to the 0.105−0.25 millimeter fraction (+140, −60 mesh) and passed over a shaking table to create a moderate to heavy mineral separate that was mounted in epoxy and subsequently analyzed using automated scanning electron microscope (SEM) techniques. Seven polished thin sections of core were also analyzed. Among the advantages of automated SEM techniques compared to visual mineral identification are that thousands of grains can be rapidly identified in each sample (about 1 hour per sample) and small quantities of indicator minerals that may be missed during traditional visual analyses can be detected. Automated SEM analyses of stream sediment and rock samples show that specific minerals (chalcopyrite, bornite, and jarosite) are indicators of potential mineralized areas. Svanbergite, an aluminum sulfate phosphate mineral, was identified in mineralized rocks and in nearly all stream sediment samples (up to 9 kilometers) downstream from the Taurus and other porphyry occurrences but not epithermal occurrences. It was not identified in areas with no known mineralization and thus it is possibly one of the best indicator minerals for porphyry copper (+/- molybdenum, gold) occurrences.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/ofr20221046","usgsCitation":"Kelley, K.D., Pfaff, K., and Graham, G.E., 2022, Results of automated scanning electron microscope (SEM) analyses of rock and stream sediment samples from the Taurus porphyry copper deposit area, Tanacross quadrangle, eastern Alaska: U.S. Geological Survey Open-File Report 2022–1046, 12 p., https://doi.org/10.3133/ofr20221046.","productDescription":"Report: vi, 12 p.; Table; Data Release","onlineOnly":"Y","ipdsId":"IP-132987","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":403682,"rank":4,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2022/1046/table1_1.csv","text":"Table 1.1","size":"12.0 kB","linkFileType":{"id":7,"text":"csv"},"description":"OFR 2022-1046 Table 1.1"},{"id":403617,"rank":3,"type":{"id":30,"text":"Data 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Geology, Geophysics, and Geochemistry Science Center
U.S. Geological Survey
P.O. Box 25046, Mail Stop 973
Denver, CO 80225
Phase 3 of the Earth Mapping Resources Initiative (Earth MRI) focuses on geologic belts that are favorable for hosting mineral systems that could contain the critical minerals antimony, barite, beryllium, chromium, fluorspar, hafnium, magnesium, manganese, uranium, vanadium, and zirconium. Prior phases of the Earth MRI program in Alaska focused only on rare earth elements, aluminum, cobalt, graphite, lithium, niobium, platinum-group metals, tantalum, tin, titanium, and tungsten. An additional 11 critical minerals planed for future phases of Earth MRI (As, Bi, Cs, Ga, Ge, In, Re, Rb, Sc, Sr, Te) are considered prospective in these focus areas. Together, Alaska focus areas address 22 of the 35 minerals or mineral material groups presently deemed critical. This report describes the methodology and techniques utilized to define focus areas for future data acquisition in Alaska; the conterminous United States are covered in a separate report.
Focus areas are identified using a mineral systems framework, which accounts for all the possible tectonic and geologic settings where co-genetic mineral deposits may form. These deposits contain many commodities, including byproduct and critical minerals. Large system-scale processes may be evaluated using such a framework to determine the influence they play on critical mineral endowment within the deposits. Analyzing larger mineral systems provides an integrated and broad context to determine how and where critical minerals are sourced, transported, and deposited in geologic systems.
Statewide geological, geochemical, geophysical, and mineral occurrence datasets informed the delineation of focus areas in Alaska. For some mineral systems, previously published data-driven prospectivity analyses for critical mineral-bearing deposit types provided the basis for focus areas. We report a total of 22 new focus areas that are prospective for phase 3 critical minerals. These new focus areas represent four different mineral systems that are known or suspected to occur in Alaska. An additional 55 focus areas that were previously identified for phase 1 and phase 2 commodities were also identified as being prospective for phase 3 critical minerals. Collectively, 102 focus areas in Alaska have known or suspected potential for hosting phase 1, phase 2, and (or) phase 3 critical minerals. These focus areas represent 17 different mineral systems also containing critical minerals that are planned for consideration in future Earth MRI phases. Thus, the focus areas delineated herein, and in previous reports for Alaska, are comprehensive for all critical minerals as presently defined and may be used to guide the collection of new geologic, geochemical, and geophysical data in the region.
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\"}}]}","contact":"Director, Alaska Science Center
U.S. Geological Survey
4210 University Drive
Anchorage, Alaska 99508
The Earth Mapping Resources Initiative (Earth MRI) is conducted in phases to identify areas for acquiring new geologic framework data to identify potential domestic resources of the 35 mineral materials designated as critical minerals for the United States. This report describes the data sources and summary results for 13 critical minerals evaluated in the conterminous United States and Puerto Rico during phase 3 of the study (antimony, barite, beryllium, chromium, fluorspar, hafnium, helium, magnesium, manganese, potash, uranium, vanadium, and zirconium). Phases 1 and 2 of the Earth MRI addressed aluminum, cobalt, graphite, lithium, niobium, platinum-group elements (PGEs), rare earth elements (REEs), tantalum, tin, titanium, and tungsten. Critical minerals in Alaska are covered in a separate report. No focus areas for phase 3 critical minerals are delineated for Hawaii.
The geologic, geochemical, topographic, and geophysical mapping provided by the Earth MRI documents geologic features that reflect the extent of individual mineral systems and provides information about critical mineral deposits that may not have been previously considered. The mineral-systems approach links critical mineral commodities to deposit types that represent the manifestations of large mineral systems.
Each of the 13 critical mineral commodities for phase 3 of the Earth MRI is discussed in terms of its importance to the Nation’s economy, modes of occurrence, mineral systems, and deposit types, and is accompanied by maps and tables listing examples of focus areas in the conterminous United States and Puerto Rico. Examples of important mineral systems for this group of 13 critical minerals include basin brine path systems for barite and fluorspar, Carlin-type systems and Coeur d’Alene systems for antimony, chemical weathering and volcanogenic seafloor systems for manganese, Climax-type systems for beryllium, mafic magmatic systems for chromium, marine evaporite systems for potash and magnesium, meteoric recharge systems for uranium, petroleum systems for helium, and placer systems for zirconium and hafnium.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191023D","collaboration":"Prepared in cooperation with the Association of American State Geologists","usgsCitation":"Hammarstrom, J.M., Dicken, C.L., Woodruff, L.G., Andersen, A.K., Brennan, S., Day, W.C., Drenth, B.J., Foley, N.K., Hall, S., Hofstra, A.H., McCafferty, A.E., Shah, A.K., and Ponce, D.A., 2022, Focus areas for data acquisition for potential domestic resources of 13 critical minerals in the conterminous United States and Puerto Rico—Antimony, barite, beryllium, chromium, fluorspar, hafnium, helium, magnesium, manganese, potash, uranium, vanadium, and zirconium, chap. 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Mineral Resources Program
U.S. Geological Survey
913 National Center
Reston, VA 20192
The Old Erie Canal has undergone sedimentation and aquatic growth that have restricted flow and diminished the aesthetic quality of the canal during the nearly 200 years since its construction. During 2018–2019, the U.S. Geological Survey (USGS) in cooperation with the Madison County Planning Department and the New York State Canal Corporation conducted a study of the Old Erie Canal between the Town of DeWitt, New York, and its junction with the current Erie Canal of the New York State Canal System near Rome, N.Y. The study comprised bathymetric, velocity, and water-quality surveys and documentation of the canal infrastructure. The USGS established benchmarks and staff gages along the 30.8 miles of the canal study area to reference the water-surface level in the canal to the North American Vertical Datum of 1988 (NAVD 88). Bathymetric survey results indicated that during the time of the survey, the canal depths ranged from 1.26 feet (ft) to 7.33 ft between the Butternut and Durhamville aqueducts (with a mean depth of 3.52 ft). Shallow depths are located throughout the canal, but the section north of the Durhamville aqueduct was the shallowest, with depths ranging from 0.68 ft to 2.44 ft (and a mean depth of 1.36 ft). The reach-averaged water velocity was 0.28 feet per second. The system generally flows west to east from the Butternut aqueduct to the entrance to the Erie Canal.
Water-quality data (dissolved oxygen, water temperature, specific conductance, pH, and turbidity) were collected concurrently with the bathymetric survey (spring 2018) to characterize changes in water quality along the length of the canal. Specific-conductance values measured upstream from the hamlet of Kirkville, Manlius, N.Y. may reflect road salts being flushed into the canal through the Butternut and Limestone feeder system (designed to divert water from nearby creeks to supply water for the Old Erie Canal) from recent stormwater runoff. Increases in pH in the downstream direction are possibly caused by increasing amounts of aquatic vegetation. During the time of the survey, turbidity was highest near inflows from the canal feeder system and tributary inputs which were elevated by stormwater runoff that transported sediment into the canal.
The canal infrastructure was documented to provide a baseline assessment. The feeder system, designed to bring water into the canal, does not deliver flow when the creeks supplying water to those feeders are at base flow, but does bring water into the system when flows in the feeder creeks are elevated. A recent report provides an example of repair work completed on the Chittenango feeder to improve flow through the feeder into the canal (Welch and Madison County Planning Department, 1996). Two non-regulated tributaries, Meadow Brook and Pools Brook, consistently delivered flow to the canal. Outfalls where canal water discharges into nearby creeks were sealed in the Butternut and Limestone aqueducts. Outfalls in the Chittenango, Cowaselon, and Durhamville aqueducts were found with flashboards installed at an elevation that allows water to be discharged from the canal. These structures are designed to accept additional flashboards to raise the canal water surface with the potential to convey flow farther down the system. The general condition of the channel was open and navigable between Butternut aqueduct and Chittenago aqueduct. On the segment of the canal east of the Chittenango aqueduct, an increasing number of downed trees and tangled wads of vegetation affected flow and made navigation by boat difficult to the Durhamville aqueduct. North of the Durhamville aqueduct, numerous downed trees and an increased density of aquatic vegetation limited navigation by boat and reduced the flow rate.
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U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349