Sea-cliff bedstraw (Galium buxifolium [Rubiaceae]) is a delicate dioecious subshrub endemic to Santa Cruz and San Miguel Islands, in the northern California Channel Islands. It was listed as endangered in 1997 under the Federal Endangered Species Act, threatened by soil loss, habitat alteration, and herbivory from more than a century of ranching land use. At the time of listing, there were eight populations known from Santa Cruz Island and two from San Miguel Island, restricted to shaded, northfacing volcanic ocean bluffs. The recovery plan included criteria to be considered for delisting such as stabilizing or increasing populations on both islands and recovery actions such as searching for more populations, seed banking, researching seed germination and life history, and reversing factors causing decline.
We began studying sea-cliff bedstraw on Santa Cruz Island in the early 2000s to better understand factors related to decline. We surveyed historic populations and searched for new occurrences 2004–06, researched population size-class structure and floral biology 2005–06 at three sites, and tracked demography 2005–14 while treating the invasive periwinkle (Vinca major) with herbicide at a fourth site. We collected seeds for germination studies and long-term seed banking from all four study sites.
The historically known Santa Cruz Island sites were still extant on the sea-cliffs, and we located 14 occurrences not documented at the time of listing. Sea-cliff bedstraw floral morphology appears evenly balanced between mainly pistillate and staminate plants at the study sites, with other floral types present as well, in the populations we sampled. Seed germination trials showed about a 40 percent germination rate from fresh imbibed seeds. Life-history stage structure 2005–06 varied from purely reproductive plants at one site to nearly equal representation of vegetative and reproductive plants at a second site, with seedlings also present at the remaining two sites.
Demographic study of tagged plants at the long-term study plot 2005–14 showed an episodic pattern of high recruitment with low mortality once plants became established beyond the first summer dormant season. A flush of seedlings was seen after periwinkle reduction, indicating that the periwinkle may have been repressing seedling establishment. About 93 percent of the already-established plants tagged in 2005 and 2006 were still present 10 years later in 2014. Findings indicate that sea-cliff bedstraw individuals are long-lived and once established have good survival rates. Taken together, these results imply that population growth is driven more by germination and recruitment into the mature population than by mortality of established plants, at least since feral animal eradication from Santa Cruz Island. Therefore, conservation actions might be best focused on nurturing safe sites for seed germination and seedling survival.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191054","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"McEachern, K., Chess, K.A., Flagg, K., and Niessen, K.G., 2019, Sea-cliff bedstraw (Galium buxifolium) patterns and trends, 2005–14, on Santa Cruz and San Miguel Islands, Channel Islands National Park, California: U.S. Geological Survey Open-File Report 2019–1054, 23 p., https://doi.org/10.3133/ofr20191054.","productDescription":"Report: vi, 23 p.; Data Release","numberOfPages":"34","onlineOnly":"Y","ipdsId":"IP-106549","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":364050,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P94J36GC","linkHelpText":"Demographic Data and Location Information for the Endangered Gailum Buxifolium from 2005 to 2014 on Santa Cruz and San Miguel Islands, California"},{"id":364030,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1054/ofr20191054.pdf","text":"Report","size":"1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Open-File Report 2019-1054"},{"id":364029,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1054/coverthb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Channel Islands, San Miguel Island, Santa Cruz Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.53649902343749,\n 33.84760762988741\n ],\n [\n -119.41589355468749,\n 33.84760762988741\n ],\n [\n -119.41589355468749,\n 34.14363482031264\n ],\n [\n -120.53649902343749,\n 34.14363482031264\n ],\n [\n -120.53649902343749,\n 33.84760762988741\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
The Fort Rock dome, in Yavapai County, Ariz., is a roughly circular geologic structure in plan view, 2.5 km in diameter, that is similar in many ways to an impact crater; however, it is a structural dome caused by a potassic mafic intrusion at depth, and the crater-like depression in its center is erosional in origin. The Aquarius Mountains, west of the Fort Rock dome, in Mohave County, contain a felsic volcanic center that erupted tuffs, non-welded ash-flow tuffs, and lahars following dome emplacement.
This report discusses the radiometric ages of samples of rock units from both the Fort Rock dome and the Aquarius Mountains eruptive center. The ages for all samples span a short interval of time from 22.3 to 21.7 m.y. (earliest Miocene). The individual sample ages are consistent with the stratigraphic order of the rock units in the area, and the short age span is consistent with the absence of any significant unconformities in the section.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191038","usgsCitation":"Fuis, G.S., Calvert, A., and Sullivan, K., 2019, Radiometric ages of volcanic rocks on the Fort Rock dome and in the Aquarius Mountains, Yavapai and Mohave counties, Arizona: U.S. Geological Survey Open-File Report 2019–1038, 18 p., https://doi.org/10.3133/ofr20191038.","productDescription":"iv, 18 p.","numberOfPages":"18","onlineOnly":"Y","ipdsId":"IP-100507","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":364031,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1038/coverthb.jpg"},{"id":364032,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1038/ofr20191038.pdf","text":"Report","size":"12.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Open-File Report 2019-1038"}],"country":"United States","state":"Arizona","county":"Yavapai County, Mohave 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Science Center
U.S. Geological Survey
345 Middlefield Road, MS 977
Menlo Park, California 94025
Telemetry has been an invaluable tool to improve our understanding of adult Pacific Lamprey (Entosphenus tridentatus) movements and to guide management approaches to protect and restore this species of concern. Juvenile and larval lamprey, however, are much smaller than adults, and have not been monitored with telemetry because available transmitters have traditionally been too large. With funding from the U.S. Department of Energy and the U.S. Army Corps of Engineers, the Pacific Northwest National Laboratory developed a prototype micro-transmitter of appropriate size for use in small fish such as juvenile lampreys and eels. Through a collaborative research approach, we used these prototype transmitters to do a pilot level evaluation of juvenile lamprey (macrophthalmia) movements in the Yakima and Columbia Rivers in 2018. Our project monitored tagged lamprey using acoustic monitoring arrays installed and maintained for juvenile salmon (Oncorhynchus spp.) migration studies done by our partners. The study was done in the lower Yakima River, Washington, from river mile 111 to the river mouth, and in the Columbia River, from the Yakima River mouth to Camas, Washington, downstream of Bonneville Dam. We released four groups of tagged lamprey from May 9 to 15, 2018. Two groups were released at the upper site (located at the State Route 24 bridge, about 4.5 river miles upstream of Wapato Dam), and two groups were released at the lower site (about 1.7 miles upstream of the Yakima River mouth). We detected 95.6 percent of the tagged lamprey, with more individuals detected in the Columbia River than in the Yakima River. Lamprey arrived at Bonneville Dam in an average of 8.0–9.6 days from the upper site (300 river miles) and in an average of 6.5 days from the lower site (193 river miles). Lamprey moved through the study area at an average rate of 30–35 miles per day and generally remained at each detection site for less than about 20 minutes. Most lamprey (63 percent) arrived at detection sites during periods of darkness, but some travel occurred during daylight and transitional light periods.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191058","collaboration":"Prepared in cooperation with Yakama Nation Fisheries, Pacific Northwest National Laboratory, McNary Fisheries Compensation Committee, and the Bureau of Reclamation","usgsCitation":"Liedtke , T.L., Lampman, R.T., Deng, D.Z., Beals, T.E., Porter, M.S., Hansen, A.C., Kock, T.J., Tomka, R.G., and Monk, P., 2019, Movements of juvenile Pacific Lamprey (Entosphenus tridentatus) in the Yakima and Columbia Rivers, Washington, 2018—A pilot study using acoustic telemetry: U.S. Geological Survey Open-File Report 2019-1058, 29 p., https://doi.org/10.3133/ofr20191058.","productDescription":"vi, 29 p.","numberOfPages":"40","onlineOnly":"Y","ipdsId":"IP-107087","costCenters":[{"id":654,"text":"Western Fisheries Research 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\"}}]}","contact":"Director, Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115-5016
The primary mission of the U.S. Geological Survey (USGS) Water Mission Area is to collect and disseminate reliable, impartial, and timely information needed to understand the water resources of the Nation, including data on streamflow, groundwater, water quality, water use, and availability. Management of data throughout the entire data lifecycle is necessary to meet the mission and maintain the USGS reputation of producing high-quality data as the Nation’s primary earth-science information agency. This document describes the data management procedures of the USGS Washington Water Science Center, including responsibilities of staff and workflow procedures.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191049","usgsCitation":"Conn, K.E., Mastin, M.C., Long, A.J., Dinicola, R.S., and Barton, C., 2019, Data management plan for the U.S. Geological Survey Washington Water Science Center : U.S. Geological Survey Open-File Report 2019-1049, 23 p., https://doi.org/10.3133/ofr20191049.","productDescription":"Report: iv, 23 p.","numberOfPages":"32","onlineOnly":"Y","ipdsId":"IP-104277","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":363807,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1049/coverthb.jpg"},{"id":363808,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1049/ofr20191049.pdf","text":"Report","size":"573 KB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1049"}],"country":"United States","state":"Washington","contact":"Director, Washington Water Science Center
U.S. Geological Survey
934 Broadway, Suite 300
Tacoma, Washington 98402
Branch Chief, Hydrologic Networks Branch, Observing Systems Division
U.S. Geological Survey
12201 Sunrise Valley Drive, MS 412
Reston, VA 20192
As part of the U.S. Department of the Interior’s (DOI) commitment to provide technical assistance to the Kenyan Northern Rangelands Trust (NRT), the U.S. Geological Survey, in collaboration with the DOI International Technical Assistance Program and the U.S. Agency for International Development’s regional mission in East Africa, created a high spatial and time-sensitive live vegetation monitoring system for NRT. The system built with advanced field and sensor technologies produced directly calibrated and highly accurate satellite mapping that is extendable both forward and backward in time. The maps are produced in a simple 0–100-percent representation of live vegetation status and change over time. The backbone of the mapping is the Sentinel satellite remote sensing systems with 5-day collection frequencies and ground spatial resolutions of 10 meters. The European Space Agency (ESA) offers free Sentinel satellite image data through conveniently accessed websites and free user-friendly image processing software downloadable directly onto a personal workstation. ESA provides free online software support. The mapping capability was extended from the forward mapping of Sentinel back in time with the Landsat satellite remote sensing system that has an available and free data archive back to 1983. Although Landsat has coarser spatial resolution, the Landsat to Sentinel live vegetation mapping comparison supports the use of Landsat to provide NRT the historical recreation of prominent live vegetation changes.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191037","collaboration":"Prepared in cooperation with the U.S. Agency for International Development","usgsCitation":"Rangoonwala, Amina, and Ramsey, E.W., III, 2019, Monitoring live vegetation in semiarid and arid rangeland environments with satellite remote sensing in northern Kenya: U.S. Geological Survey Open-File Report 2019–1037, 83 p., https://doi.org/10.3133/ofr20191037.","productDescription":"Report: vii, 83 p.; 15 Figures","numberOfPages":"96","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-105119","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":363609,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1037/ofr20191037.pdf","text":"Report","size":"22.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019–1037"},{"id":363608,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1037/coverthb.jpg"},{"id":363610,"rank":3,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/of/2019/1037/ofr20191037_fig15a.tif","text":"Figure 15A—high resolution—","description":"OFR 2019–1037 Figure 15A","linkHelpText":"June 2018 live vegetation map"},{"id":363617,"rank":10,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/of/2019/1037/ofr20191037_fig18b.tif","text":"Figure 18B—high resolution—","description":"OFR 2019–1037 Figure 18B","linkHelpText":"Live cover map with tree mask overlay (dark green)"},{"id":363611,"rank":4,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/of/2019/1037/ofr20191037_fig15b.tif","text":"Figure 15B—high resolution—","description":"OFR 2019–1037 Figure 15B","linkHelpText":"June 2018 live vegetation map with tree mask overlay (dark green)"},{"id":363612,"rank":5,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/of/2019/1037/ofr20191037_fig16a.tif","text":"Figure 16A—high resolution—","description":"OFR 2019–1037 Figure 16A","linkHelpText":"September 2017 live vegetation map"},{"id":363613,"rank":6,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/of/2019/1037/ofr20191037_fig16b.tif","text":"Figure 16B—high resolution—","description":"OFR 2019–1037 Figure 16B","linkHelpText":"September 2017 live vegetation map with tree mask overlay (dark green)"},{"id":363614,"rank":7,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/of/2019/1037/ofr20191037_fig17a.tif","text":"Figure 17A—high resolution—","description":"OFR 2019–1037 Figure 17A","linkHelpText":"June 2017 live vegetation cover proportion map"},{"id":363615,"rank":8,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/of/2019/1037/ofr20191037_fig17b.tif","text":"Figure 17B—high resolution—","description":"OFR 2019–1037 Figure 17B","linkHelpText":"June 2017 live vegetation cover map with tree mask overlay (dark green)"},{"id":363616,"rank":9,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/of/2019/1037/ofr20191037_fig18a.tif","text":"Figure 18A—high resolution—","description":"OFR 2019–1037 Figure 18A","linkHelpText":"June 2017 to June 2018 live vegetation cover proportion change map"},{"id":363618,"rank":11,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/of/2019/1037/ofr20191037_fig19a.tif","text":"Figure 19A—high resolution—","description":"OFR 2019–1037 Figure 19A","linkHelpText":"September 2017 to June 2018 live vegetation cover proportion change map"},{"id":363619,"rank":12,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/of/2019/1037/ofr20191037_fig19b.tif","text":"Figure 19B—high resolution—","description":"OFR 2019–1037 Figure 19B","linkHelpText":"Live cover maps with tree mask overlay (dark green)"},{"id":363620,"rank":13,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/of/2019/1037/ofr20191037_fig20a.tif","text":"Figure 20A—high resolution—","description":"OFR 2019–1037 Figure 20A","linkHelpText":"June 2017 to September 2017 live vegetation cover proportion change map"},{"id":363621,"rank":14,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/of/2019/1037/ofr20191037_fig20b.tif","text":"Figure 20B—high resolution—","description":"OFR 2019–1037 Figure 20B","linkHelpText":"Live vegetation change map with tree mask overlay (dark green)"},{"id":363622,"rank":15,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/of/2019/1037/ofr20191037_fig40a.tif","text":"Figure 40A—high resolution—","description":"OFR 2019–1037 Figure 40A","linkHelpText":"Live vegetation cover proportion for the core-Kenyan Northern Rangelands Trust conservancies in June 2017 "},{"id":363623,"rank":16,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/of/2019/1037/ofr20191037_fig40b.tif","text":"Figure 40B—high resolution—","description":"OFR 2019–1037 Figure 40B","linkHelpText":"Live vegetation cover proportion for the core-Kenyan Northern Rangelands Trust conservancies in June 2018 "},{"id":363624,"rank":17,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/of/2019/1037/ofr20191037_fig41.jpg","text":"Figure 41—high resolution—","description":"OFR 2019–1037 Figure 41","linkHelpText":"June 2018 synthetic aperture radar (SAR) vertical send and vertical receive (VV) and vertical send and horizontal receive (VH) images"}],"country":"Kenya","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 33.8818359375,\n -0.9667509997666298\n ],\n [\n 37.72705078125,\n -3.601142320158722\n ],\n [\n 39.26513671875,\n -4.8282597468669755\n ],\n [\n 40.166015625,\n -3.3160183381615123\n ],\n [\n 41.72607421875,\n -1.7794990011582128\n ],\n [\n 41.02294921875,\n 1.0765967983064109\n ],\n [\n 42.22045898437501,\n 4.313546364068527\n ],\n [\n 40.78125,\n 4.280680030820496\n ],\n [\n 38.97949218749999,\n 3.71078200434872\n ],\n [\n 35.244140625,\n 4.872047700241915\n ],\n [\n 33.42041015625,\n 4.313546364068527\n ],\n [\n 33.8818359375,\n -0.9667509997666298\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Wetland and Aquatic Research Center
U.S. Geological Survey
700 Cajundome Blvd.
Lafayette, LA 70506
Topographical differencing and edge-matching analyses were used to evaluate agreement of the Coastal National Elevation Database Applications Project’s Northern Gulf of Mexico topobathymetric digital elevation model (TBDEM) with The National Map 3-Dimensional Elevation Program (3DEP) 1/3 arc-second digital elevation models (DEMs). In addition to topographic map products provided through the National Geospatial Program, the model integrates bathymetric and topobathymetric datasets for three-dimensional (3D) mapping of rivers, lakes, and bays in the upland and intertidal wetlands to offshore environments in coastal zones from the border between Texas and Louisiana to east of Mobile Bay, Alabama.
Contoured elevation differences between the Northern Gulf of Mexico TBDEM and the 3DEP 1/3 arc-second DEMs indicate that 85 percent of elevation data in the Northern Gulf of Mexico TBDEM agree (no difference for contoured elevations) between 95 and 100 percent with 3DEP 1/3 arc-second DEMs. Edge matching differences between adjacent Northern Gulf of Mexico TBDEM source projects or between the TBDEM and 3DEP DEMs indicate most seams between integrated and 3DEP DEMs are smooth. Where seams did not match, most differences were in the range of tenths to hundredths of a meter. Valid differences that are greater than plus or minus 2 meters in areas of bathymetric data are found in the Mississippi River, Atchafalaya River, Lower Atchafalaya River, Wax Lake Pass channel, the Vermilion Bay bathymetric datasets, and where topobathymetric datasets are integrated in the model. Areas with positive or negative outlier difference elevations seem to be a result of site conditions that affect light detection and ranging (lidar) waveform return signals, misclassification of surface features, or possibly because of interpolation required to develop a smooth elevation surface. Results of this analysis provide information to help understand model parameters and agreement of the Northern Gulf of Mexico TBDEM developed using different data types from different sources with The National Map 3DEP DEMs.
Inclusion of bathymetric and topobathymetric data types in the 3DEP aligns with the mission to respond to growing needs for a wide range of three-dimensional representations of the Nation and supports the U.S. Geological Survey strategy for developing a National Terrain Model to provide hydrographic and elevation data that extend the elevation surface below water bodies. The 3D Nation Requirements and Benefits Study sponsored by the U.S. Geological Survey and National Oceanic and Atmospheric Administration to assess local to regional Tribal, State, and Federal technical requirements, needs, and benefits for using topographic and bathymetric 3DEP elevation data will be used to help develop and refine future program alternatives for 3D elevation data that include a category for bathymetry and topobathymetry. At the time of this report (2019), 3DEP acquisition is specific to topographic lidar that meets lidar DEM specifications and which requires surface-water feature areas to be hydroflattened. Cataloging bathymetric and topobathymetric DEMs as part of the 3DEP will require new specifications for acoustic, lidar, merged acoustic and lidar, and possibly other bathymetric and topobathymetric survey data types.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191016","usgsCitation":"Miller-Corbett, C., 2019, Analysis for agreement of the Northern Gulf of Mexico topobathymetric digital elevation model with 3-Dimensional Elevation Program 1/3 arc-second digital elevation models: U.S. Geological Survey Open-File Report 2019–1016, 44 p., https://doi.org/10.3133/ofr20191016.","productDescription":"vi, 43 p.","numberOfPages":"54","onlineOnly":"Y","ipdsId":"IP-081383","costCenters":[{"id":404,"text":"NGTOC Rolla","active":true,"usgs":true}],"links":[{"id":363655,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1016/ofr20191016.pdf","text":"Report","size":"16.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019–1016"},{"id":363654,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1016/coverthb.jpg"}],"country":"United States","state":"Alabama, Florida, Louisiana, Mississippi, Texas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -96.48193359375,\n 28.43971381702788\n ],\n [\n -84.13330078125,\n 28.43971381702788\n ],\n [\n -84.13330078125,\n 31.39115752282472\n ],\n [\n -96.48193359375,\n 31.39115752282472\n ],\n [\n -96.48193359375,\n 28.43971381702788\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, National Geospatial Technical Operations Center
U.S. Geological Survey
1400 Independence Road
Rolla, MO 65401
Waterfowl population management and habitat conservation compose one of the oldest and most successful adaptive management frameworks in the world. Since its inception, the North American Waterfowl Management Plan (NAWMP) has emphasized strategically targeted conservation investments in regions that most affect waterfowl population dynamics. By 2012, regional conservation had progressively become more science-based and strategic: many migratory bird partnerships had initiated or completed projects on mapping and modeling waterfowl distribution and abundances using geospatial techniques. However, when developing a map depicting and titled “Areas of Greatest Continental Significance to North American Ducks, Geese, and Swans” for the 2012 NAWMP Revision, waterfowl professionals articulated the need for improved decision frameworks and use of consistent datasets for refining large-scale spatial products depicting priority areas for waterfowl and people. This report describes a framework for developing a spatial value model to support the identification of North American geographies of importance to waterfowl during the breeding and non-breeding periods and to resource users who could potentially support (financially and (or) politically) waterfowl habitat conservation. Objectives used to identify priority geographies were determined through a collaborative process of the NAWMP Science Support Team, Priority Landscapes Committee (PLC), and other experts in the fields of waterfowl biology and ecology, environmental science, and human dimensions. ArcGIS Desktop was used as the platform for managing, analyzing, combining and displaying the spatial data as well as producing new data through spatial analysis functions. Thirty-eight spatial layers were developed, and several composite spatially explicit products (maps of North America) were produced based on PLC recommendations. The composite products have extensive similarities to the 2012 NAWMP map depicting areas of greatest continental significance to North American waterfowl. There are also some differences, especially in regions of the high Arctic and in Mexico. These differences between spatial value model maps and the 2012 NAWMP output likely arose from inclusion of social objectives, reduced dependence on expert opinion to generate abundance estimates, lack of population surveys in some regions and availability of expanded survey data in other regions, and use of model-based waterfowl population estimates for some unsurveyed areas.
The structured decision-making framework application in this study is discussed, and the appropriate use of the products and their limitations are outlined. Additionally, options for future improvements are presented by identifying gaps in data collection, waterfowl-habitat association assumptions, and uncertainties related to social objectives. These spatial products are intended for use by national, regional, and province/state level wildlife professionals to aid their decisions in targeting waterfowl habitat conservation.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191029","usgsCitation":"Krainyk, A., Lyons, J.E., Brasher, M.G., Humburg, D.D., Soulliere, G.J., Coluccy, J.M., Petrie, M.J., Howerter, D.W., Slattery, S.M., Rice, M.B., and Fuller, J.C., 2019, Spatial integration of biological and social objectives to identify priority landscapes for waterfowl habitat conservation: U.S. Geological Survey Open-File Report 2019–1029, 41 p., https://doi.org/10.3133/ofr20191029.","productDescription":"Document: vii, 41 p.; Additional Report Piece; Data Release","numberOfPages":"53","ipdsId":"IP-100747","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":363506,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9L7J5U4","text":"USGS data release","description":"USGS data release","linkHelpText":"Spatial Integration of Biological and Social Objectives to Identify Priority Landscapes for Waterfowl Habitat Conservation"},{"id":363574,"rank":3,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://pubs.usgs.gov/of/2019/1029/ofr20191029_supplementalinformation.pdf","text":"Supplemental Information","size":"5.41 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":363503,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1029/coverthb2.jpg"},{"id":363504,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1029/ofr20191029.pdf","text":"Report","size":"30.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1029"}],"contact":"Director, Eastern Ecological Science Center
U.S. Geological Survey
12100 Beech Forest Road, Ste 4039
Laurel, MD 20708
An evaluation was conducted to describe adult sockeye salmon (Oncorhynchus nerka) upstream movement patterns from Roza Dam to Cle Elum Dam in the Yakima Basin, Washington. Sockeye salmon adults that arrive at Roza Dam are currently trapped and transported upstream of Cle Elum Dam because upstream fish-passage facilities are not currently in place at the dam. However, these facilities are being designed, so resource managers wanted to confirm that sockeye salmon could successfully move upstream through the Roza Dam-to-Cle Elum Dam reach. A total of 20 adult sockeye salmon were collected, tagged with a radio transmitter, and released at Roza Dam during July 2018. These fish were monitored using a series of fixed monitoring sites and mobile tracking for 2 months. All tagged sockeye salmon successfully moved upstream and were detected in the tailrace of Cle Elum Dam. Median elapsed time from release at Roza Dam to first detection at Cle Elum Dam was 17 days (d) and ranged from 7 to 30 d. We examined migration delay at the Town Ditch diversion dam by comparing elapsed time of detection at that site to elapsed time of detection at other sites in the study area. We found that median elapsed time at the Town Ditch site (19.7 hours) was substantially higher than median elapsed time at other sites (1.7 hours or less). This suggests that tagged sockeye salmon were delayed at the diversion dam. However, most tagged fish (75 percent) arrived at Cle Elum Dam within 20 d of release, and all fish arrived within 30 d, which suggests that delay at the diversion dam may be of little consequence. Our results suggest that adult sockeye salmon can readily migrate upstream from Roza Dam to Cle Elum Dam.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191053","collaboration":"Prepared in cooperation with the Yakama Nation, Bureau of Reclamation, and Washington State Department of Ecology","usgsCitation":"Kock, T.J., Evans, S.D., Ekstrom, B.K., and Hansen, A.C., 2019, Adult sockeye salmon (Oncorhynchus nerka) behavior and movement from Roza Dam to Cle Elum Dam, Washington, 2018: Open-File Report 2019-1053, 8 p., https://doi.org/10.3133/ofr20191053.","productDescription":"iv, 8 p.","numberOfPages":"16","onlineOnly":"Y","ipdsId":"IP-101922","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":363570,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1053/ofr20191053.pdf","text":"Report","size":"3.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1053"},{"id":363569,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1053/coverthb.jpg"}],"country":"United States","state":"Washington","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.06933593749999,\n 45.97406038956237\n ],\n [\n -121.06933593749999,\n 47.33510005753562\n ],\n [\n -119.783935546875,\n 47.33510005753562\n ],\n [\n -119.783935546875,\n 45.97406038956237\n ],\n [\n -121.06933593749999,\n 45.97406038956237\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115-5016
The combination of rainfall and snowmelt in northern New England and rainfall in southern New England resulted in minor to major flooding from April 15 to 24, 2019, according to stage and streamflow data collected at 63 selected U.S. Geological Survey (USGS) streamgages. A typical USGS streamgage measures and records stream stage and estimates streamflow based on a relation (rating curve) of discrete measurements of streamflow and the recorded stage. USGS hydrographers were deployed during and after these storms to measure the streamflow of the flooded rivers and confirm streamgage rating curves.
Preliminary Data Indicate...
Director,
New England Water Science Center
U.S. Geological Survey
331 Commerce Way, Suite 2
Pembroke, NH 03275
The Sacramento metropolitan (SacMetro) study unit covers approximately 3,250 square kilometers of the Central Valley along the eastern edge of the northern and southern ends of the San Joaquin and Sacramento Valleys, respectively. Groundwater withdrawals supply a significant portion of the water-resource needs of the region. In the southern portion of the study unit, groundwater accounts for nearly 90 percent of water demand in the area (South Area Water Council, 2011).
Groundwater sampled in the SacMetro study unit comes from alluvial aquifers primarily composed of sediments derived from the Sierra Nevada Mountains to the east. Recharge to the groundwater system is primarily from the streams draining the Sierra Nevada, and from precipitation and infiltration of applied irrigation water (California Department of Water Resources, 2003). The public-supply aquifer system assessments of this area in 2005 found elevated concentrations of inorganic constituents including arsenic, iron, and manganese as well as of solvents in some wells (Bennett and others, 2010; 2011).
This study was designed to provide a statistically representative assessment of the quality of groundwater resources used for domestic drinking water in the SacMetro study unit. A complete listing of what was measured, including the sampling results, are presented in Bennett and others, 2019. A total of 49 wells were sampled between July 2017 and November 2017 (Bennett and others, 2019). The wells in the study were 32–160 meters deep, and water levels were 1–62 meters below land surface.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191047","collaboration":"Prepared in cooperation with the California State Water Resources Control Board","usgsCitation":"Bennett, G.L. V, 2019, Groundwater quality in the Sacramento Metropolitan shallow aquifer, California: U.S. Geological Survey Open-File Report 2019–1047, 4 p., https://doi.org/10.3133/ofr20191047.","productDescription":"4 p.","numberOfPages":"4","ipdsId":"IP-102366","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":437473,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9BPGEGH","text":"USGS data release","linkHelpText":"Groundwater-quality data in the Sacramento Metro shallow aquifer study unit, 2017: Results from the California GAMA Priority Basin Project"},{"id":363457,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1047/coverthb.jpg"},{"id":363458,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1047/ofr20191047_.pdf","text":"Report","size":"2.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Open-File Report 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California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
Approaches and techniques for control of exotic annual grasses are a high priority in sagebrush-steppe and other rangelands. Strains of the soil bacterium Pseudomonas fluorescens (Pf) have been proposed to be selectively pathogenic to multiple species of exotic annual grasses with effects evident by the second year, and with no effect on native or desirable species including native bunchgrasses. However, scientifically defensible tests of the target and non-target/risk effects of these hypothetically weed-suppressive bacteria (WSB) strains in the field have been lacking in rangelands and other environments. We evaluated the effects of two strains of Pf WSB (D7 and MB906) sprayed on the surface in autumn 2016 at three sites in sagebrush steppe across southwestern Idaho that had cheatgrass (Bromus tectorum), medusahead (Taeniatherum caput-medusae), and other exotic annual grasses. Treatments also were replicated within each site (n=3, 8.3×8.3 meter plots) and included evaluation of the WSB strains with and without herbicides (imazapic and rimsulfuron) and with or without discing to mix surface-spray of the WSB into deeper soils. By the second year following application (spring 2018), neither strain of WSB affected exotic annual grasses, perennial bunchgrasses, or total community cover, either with WSB alone or in combination with herbicides or discing. We conclude that neither the D7 nor MB906 strains of Pf WSB have a negative effect on exotic annuals at the sites we evaluated.
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\"}}]}","contact":"Director, Forest and Rangeland Ecosystem Science Center
U.S. Geological Survey
777 NW 9th St., Suite 400
Corvalis, Oregon 97330
Thousands of screened water diversions throughout the Columbia River Basin of the Pacific Northwest are sources of entrainment (unintended diversion into an unsafe passage route), injury, and mortality for a range of fish species and screening criteria have been developed to reduce and mitigate these effects. Large knowledge gaps exist concerning the potential effects of these screens on juvenile and larval lampreys (Entosphenus and Lampetra spp.) that may be particularly vulnerable to screening effects owing to their small size, unique morphology, and poor swimming performance. The few studies that have evaluated screen impacts for lampreys have compared common screen materials in a laboratory setting using a large, recirculating flume, and have reported that screen size and material influence the risk of entrainment. We compared entrainment rate, impingement rate and duration, injury rate, and delayed (24-hour) mortality of larval lamprey (ammocoetes) exposed to two screen angles. A 20-degree screen was tested because it is a common configuration, and a 12-degree screen was selected to represent a screen more parallel to flow than the 20-degree screen. We included juvenile rainbow trout (Oncorhynchus mykiss) in tests for both screen angles so that any screen interaction improvements observed for lamprey could be considered in light of their effect on salmonids (Oncorhynchus spp.). Study fish were released singly into the flume at two locations: near-screen (SCR) releases at about 30 centimeters upstream of the screen and mid-channel (MID) releases at about 1.4 meters upstream of the screen. We completed 120 screen trials with ammocoetes and 117 screen trials with juvenile tout during April–May 2018. Ammocoetes had short mean trial durations (less than 30 seconds) and were most frequently bypassed. Over one-half of the ammocoetes briefly (for less than 1 second) contacted the screen. Rates of screen contact were similar between the two screen angles, but higher for SCR releases (about 83–90 percent) than for MID releases (about 28–44 percent). Screen impingements were not common (8.3 percent of ammocoetes) and did not result in significant injury or delayed mortality. The final logistic regression model showed that screen angle and release location did not significantly affect whether ammocoetes were bypassed. The size of the lamprey, expressed as mass or length, was the only significant predictor of fate. We noted an estimated 4.7-percent increase in the odds of being bypassed for every 1-milligram increase in ammocoete mass and a 49-percent increase in the odds of being bypassed for every 1-millimeter increase in length. Trout did not experience negative effects with the 12-degree screen; they had short mean trial durations (less than 40 seconds) and limited contact with the screens (11.1 percent of trout), were most commonly bypassed, and none were entrained. Screen angle and release location were not significant drivers of fate for trout. The 12-degree and 20-degree screens performed comparably for both lamprey and trout and effectively bypassed these fishes. Some metrics showed advantages for the 12-degree screen, suggesting that screens installed more parallel to flow might warrant further testing.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191044","collaboration":"Prepared in cooperation with the Washington Department of Fish and Wildlife, McNary Fisheries Compensation Committee, and Yakama Nation Fisheries","usgsCitation":"Liedtke, T.L., Didricksen, D.J., Weiland, L.K., Rogala, J.A., and Lampman, R., 2019, Effectiveness of fish screens in protecting lamprey (Entosphenus and Lampetra spp.) ammocoetes—Pilot testing of variable screen angle: U.S. Geological Survey Open-File Report 2019-1044, 21 p., https://doi.org/10.3133/ofr20191044.","productDescription":"iv, 21 p.","numberOfPages":"30","onlineOnly":"Y","ipdsId":"IP-105978","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":363436,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1044/coverthb2.jpg"},{"id":363437,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1044/ofr20191044.pdf","text":"Report","size":"882 KB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1044"}],"country":"United States","otherGeospatial":"Columbia River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -127.57324218750001,\n 41.96765920367816\n ],\n [\n -112.763671875,\n 41.96765920367816\n ],\n [\n -112.763671875,\n 50.90303283111257\n ],\n [\n -127.57324218750001,\n 50.90303283111257\n ],\n [\n -127.57324218750001,\n 41.96765920367816\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115-5016
The degradation of coastal habitats, particularly coral reefs, raises risks by increasing the exposure of coastal communities to flooding hazards. The protective services of these natural defenses are not assessed in the same rigorous economic terms as artificial defenses, such as seawalls, and therefore often are not considered in decision making. Here we combine engineering, ecologic, geospatial, social, and economic tools to provide a rigorous valuation of the coastal protection benefits of all U.S. coral reefs in the States of Hawaiʻi and Florida, the territories of Guam, American Samoa, Puerto Rico, and Virgin Islands, and the Commonwealth of the Northern Mariana Islands. We follow risk-based valuation approaches to map flood zones at 10-square-meter resolution along all 3,100+ kilometers of U.S. reef-lined shorelines for different storm probabilities to account for the effect of coral reefs in reducing coastal flooding. We quantify the coastal flood risk reduction benefits provided by coral reefs across storm return intervals using the latest information from the U.S. Census Bureau, Federal Emergency Management Agency, and Bureau of Economic Analysis to identify their annual expected benefits, a measure of the annual protection provided by coral reefs. Based on these results, the annual protection provided by U.S. coral reefs is estimated in:
Thus, the annual value of flood risk reduction provided by U.S. coral reefs is more than 18,000 lives and \\$1.805 billion in 2010 U.S. dollars. These data provide stakeholders and decision makers with spatially explicit, rigorous valuation of how, where, and when U.S. coral reefs provide critical coastal storm flood reduction benefits. The overall goal is to ultimately reduce the risk to, and increase the resiliency of, U.S. coastal communities.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191027","collaboration":"Prepared in cooperation with the University of California Santa Cruz and The Nature Conservancy","usgsCitation":"Storlazzi, C.D., Reguero, B.G., Cole, A.D., Lowe, E., Shope, J.B., Gibbs, A.E., Nickel, B.A., McCall, R.T., van Dongeren, A.R., and Beck, M.W., 2019, Rigorously valuing the role of U.S. coral reefs in coastal hazard risk reduction: U.S. Geological Survey Open-File Report 2019–1027, 42 p., https://doi.org/10.3133/ofr20191027.","productDescription":"Report: vi, 42 p.","numberOfPages":"52","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-103961","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":363119,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9KMH2VX","linkHelpText":"Projected flooding extents and depths based on 10-, 50-, 100-, and 500-year wave-energy return periods, with and without coral reefs, for the States of Hawaii and Florida, the Territories of Guam, American Samoa, Puerto Rico, and the U.S. Virgin Islands, and the Commonwealth of the Northern Mariana Islands"},{"id":363114,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1027/ofr20191027.pdf","text":"Report","size":"10.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Open-File Report 2019-1027"},{"id":363113,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1027/coverthb.jpg"}],"country":"United States","state":"American Samoa, Commonwealth of the Northern Mariana Islands, Florida, Guam, Hawaii, Puerto Rico, U.S. Virgin Islands","contact":"Contact Information,
Pacific Coastal and Marine Science Center
U.S. Geological Survey
Pacific Science Center
2885 Mission St.
Santa Cruz, CA 95060
California provides wintering habitat for most greater white-fronted geese (Anser albifrons [GWFG]) in the Pacific Flyway and this population has rapidly increased since the 1980s. Increased harvest of GWFG wintering in California may prevent agricultural depredation while providing increased hunting opportunities. However, changes in harvest levels are unlikely to be uniform across the species because of the presence of multiple subspecies of GWFG in the Pacific Flyway, each with their own population distribution and trends. White-fronted geese in the Cook Inlet Basin of south-central Alaska, a potentially vulnerable subspecies (Tule goose, A. a. elgasi), are among the geese that winter predominantly in the Sacramento Valley and Suisun and Napa marshes of north-central California. Efforts to limit sport harvest of Tule geese are complicated because although the subspecies is phenotypically larger and darker in color than other subspecies, they can be difficult to identify in the field and in hunter bag checks. To assist in an accurate assessment of Tule goose harvest, we used double-digest restriction site-associated deoxyribonucleic acid sequencing (ddRAD-seq) techniques to develop a genetic stock identification panel of single nucleotide polymorphisms (SNPs) to differentiate Tule geese from individuals belonging to other GWFG subspecies and populations that winter in California. Although the panel we developed was designed and tested for Fluidigm SNP-type technology, the ddRAD-seq sequences can be used to design SNP panels for use in other platforms.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191040","collaboration":"Prepared in cooperation with the California Department of Fish and Wildlife","usgsCitation":"Wilson, R.E., Sonsthagen, S.A., DaCosta, J.M., Ely, C.R., Sorenson, M.D., and Talbot, S.L., 2019, Identification of single nucleotide polymorphisms for use in a genetic stock identification system for greater white-fronted goose (Anser albifrons) subspecies wintering in California: U.S. Geological Survey Open-File Report 2019-1040, 18 p., https://doi.org/10.3133/ofr20191040.","productDescription":"Report: iv, 18 p.; Data Release","numberOfPages":"26","onlineOnly":"Y","ipdsId":"IP-106743","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":363313,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9LYUFRH","text":"USGS data report","description":"USGS Data Report","linkHelpText":"Development of Single Nucleotide Polymorphisms (SNPs) in Greater-White Fronted Geese (Anser albifrons) for genetic stock identification on wintering grounds, 2019"},{"id":363317,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1040/coverthb.jpg"},{"id":363318,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1040/ofr20191040.pdf","text":"Report","size":"2.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1040"}],"country":"United States","state":"Alaska, California","contact":"Director, Alaska Science Center
U.S. Geological Survey
4210 University Drive
Anchorage, Alaska 99508
Director, Eastern Mineral and Energy Resources Center
U.S. Geological Survey
MS 954 National Center
12201 Sunrise Valley Drive
Reston, Virginia 20192
A lagoon-wide, point-sampling survey of eelgrass (Zostera marina) abundance was conducted in Izembek Lagoon, Alaska, August 7–16, 2018, the ninth year of annual surveys (2007–11, 2015–18). Mean predicted aboveground biomass of eelgrass across 116 sampled points was 238 grams per square meter (g m-2) (95 percent confidence interval: 203–278 g m-2) in 2018, an increase of 240 percent from the previous year’s low estimate of 97 g m-2 (95 percent confidence interval: 78–120 g m-2). The increase marked the third year since 2015 where eelgrass biomass was above the long-term mean (158 g m-2). Eelgrass biomass was stable over the 9 years of this survey. A separate (transect) survey for eelgrass abundance at Grant Point-Old Boat Launch showed annual trends in eelgrass biomass similar to the lagoon-wide survey, but over a slightly longer time (2007–18). The estimates of above-average eelgrass biomass in Izembek Lagoon were likely influenced by relatively warm air temperatures and little or no ice in winter (air temperatures 2.7 degrees Celsius greater than the 12-year mean) and average (cool) air temperatures during the growing season (April–August) in 2018.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191042","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service","usgsCitation":"Ward, D.H., and Amundson, C.L., 2019, Monitoring annual trends in abundance of eelgrass (Zostera marina) at Izembek National Wildlife Refuge, Alaska, 2018: U.S. Geological Survey Open-File Report 2019-1042, 8 p., https://doi.org/10.3133/ofr20191042.","productDescription":"iv, 8 p.","numberOfPages":"16","onlineOnly":"Y","ipdsId":"IP-105984","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":437489,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P13EG9KS","text":"USGS data release","linkHelpText":"Eelgrass Biomass Model"},{"id":363193,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1042/ofr20191042.pdf","text":"Report","size":"532 KB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1042"},{"id":363192,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1042/coverthb.jpg"}],"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 -163.42987060546875,\n 55.02802211299252\n ],\n [\n -162.46856689453125,\n 55.02802211299252\n ],\n [\n -162.46856689453125,\n 55.51774716789874\n ],\n [\n -163.42987060546875,\n 55.51774716789874\n ],\n [\n -163.42987060546875,\n 55.02802211299252\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Alaska Science Center
U.S. Geological Survey
4210 University Drive
Anchorage, Alaska 99508
Adaptive management has been applied to problems with multiple conflicting objectives in various natural resources settings to learn how management actions affect divergent values regarding system response. Hydropower applications have only recently begun to emerge in the field, yet in the specific example reported herein, stakeholders invested in determining the best management alternatives for attainment of a suite of objectives outlined in a long-term adaptive management program below R.L. Harris Dam, a large, privately owned dam in Alabama. Stakeholders convened an objective-setting workshop to engage a governance structure and developed a decision support model to determine appropriate actions that optimized stakeholder values. The process led to implemented change in dam operation inclusive of incorporating hypothetical responses in system parameters to management. To account for the iterative loop of adaptive management, yearly monitoring of state variables that approximated many stakeholder objectives was performed from 2005 to 2016 and data collected were incorporated into the decision model. Specific analysis of fish and macroinvertebrate population responses indicated a less than satisfactory response for some stakeholders to the flow-management changes at the dam. Uncertainty regarding the best management to provide adequate hydrologic and thermal habitats for fauna and boatable days for recreationists still exists. The project led to a Federal Energy Regulatory Commission process for renewing the license to operate the dam (beginning in 2018); adaptive management could be a viable path forward to ensure stakeholder satisfaction related to new management options.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191026","collaboration":"Prepared in cooperation with the Alabama Department of Conservation and Natural Resources, Alabama Power Company, U.S. Fish and Wildlife Service, and R.L. Harris Dam Adaptive Management Stakeholders","usgsCitation":"Irwin, E.R., ed., 2019, Adaptive management of flows from R.L. Harris Dam (Tallapoosa River, Alabama)—Stakeholder process and use of biological monitoring data for decision making: U.S. Geological Survey Open-File Report 2019–1026, 93 p., https://doi.org/10.3133/ofr20191026.","productDescription":"Report: x, 93 p.; 4 Appendixes; 1 Table","numberOfPages":"108","onlineOnly":"Y","ipdsId":"IP-096592","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":363058,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2019/1026/ofr20191026_appendix_A2.pdf","text":"Appendix A2","size":"302 kB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019–1026 Appendix A2","linkHelpText":"– Initial Bayesian Belief Network (2005), Training Cases and Learned Networks (2005–16)"},{"id":363057,"rank":2,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2019/1026/ofr20191026_appendix_A1.pdf","text":"Appendix A1","size":"1.14 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019–1026 Appendix A1","linkHelpText":"– Transcripts from the Adaptive Management Workshop, April 30–May 1, 2003"},{"id":363061,"rank":6,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2019/1026/ofr20191026_table_C2.1.pdf","text":"Table C2.1","size":"198 kB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019–1026 Table C2.1","linkHelpText":"– Sum of total observations for each macroinvertebrate taxon at all sites, listed alphabetically by class, order, family and taxon"},{"id":363060,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2019/1026/ofr20191026_appendix_B.pdf","text":"Appendix B","size":"296 kB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019–1026 Appendix B","linkHelpText":"– R code used to conduct metapopulation analyses"},{"id":363056,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1026/ofr20191026.pdf","text":"Report","size":"5.82 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019–1026"},{"id":363053,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1026/coverthb3.jpg"},{"id":363059,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2019/1026/ofr20191026_appendix_A3.pdf","text":"Appendix A3","size":"112 kB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019–1026 Appendix A3","linkHelpText":"– Charter of the R.L. Harris Stakeholders Board"}],"country":"United States","state":"Alabama","otherGeospatial":"Tallapoosa River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -85.7208251953125,\n 32.93953889877841\n ],\n [\n -85.48324584960936,\n 32.93953889877841\n ],\n [\n -85.48324584960936,\n 33.6283419913718\n ],\n [\n -85.7208251953125,\n 33.6283419913718\n ],\n [\n -85.7208251953125,\n 32.93953889877841\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Alabama Cooperative Fish and Wildlife Research Unit
School of Forestry and Wildlife Sciences
Auburn University
602 Duncan Dr.
Auburn, AL 36849–5418
The U.S. Geological Survey, the National Oceanic and Atmospheric Administration, the European Association for Remote Sensing Companies, and the European Space Agency in coordination with the GEOValue Community hosted a side event to the Group on Earth Observations Plenary on October 23–24, 2017, in Washington, D.C. The workshop, entitled “Demonstrating the Value of Earth Observations: Methods, Practical Applications and Solutions,” brought together more than 60 international experts including economists, scientists, and engineers to consider the state of the science and applications of valuing Earth observations (EO).
This 2-day workshop built upon previous activities developed under the GEOValue initiative. This workshop brought together expert analysts from multiple disciplines and backgrounds who are developing methods to identify and measure the value of information generated from the use of satellite and in-situ data. The mix of government agencies, international financial institutions, and independent consultants who participated in the workshop blended to develop a rich mix of views, approaches, and outcomes.
During the first part of the workshop, the focus was on the latest science in valuing EO. A number of methodologies were described. Approaches generally assess the societal benefits of specific actions (for example, investments in EO). Some methods focus on broad measures of economic activity (for example, gross domestic product) or methods to assess total economic value such as contingent valuation surveys. Alternatively, use-case approaches (a use case is defined as an evaluation in which one or more decisions, applications, or other uses of data, information, and information products are specifically considered) start with the specific actions and how information is used to support decision making and affect outcomes.
The second part of the meeting was focused on the use and development of value chains and decision trees. A value chain can be defined as the set of value-adding activities that one or more organizations perform in creating and distributing goods and services. In terms of EO, the value chain approach can be applied to consider societal benefits of the data and assess the value of data and data features. The EO value chain considers the geospatial data sources and the processing of the data into value added information to be incorporated into decision-support systems, leading to decision makers’ actions. To understand the value of EO, one would also need to recognize the demand side of the equation or how EO benefits users. Extending the value chain concept and incorporating tenets of Bayesian decision making, a decision tree would include one or more use cases. The value provided by the marginal increase in information could flow from one or several parts of the supply side of the value chain. The decision tree is based on the premise that information has no value if it is not used in at least one decision. By connecting the value chain and the decision tree, a framework is created that allows for conceptualizing the value of EO in its many uses. One can then apply economic techniques to monetize the marginal benefit of an outcome with information versus one without.
A third part of the meeting applied the value chain and decision-tree frameworks to five specific thematic areas, each with the focus of using information for a decision point:
During the working session, five separate groups worked to define and delineate the value chains and decision trees associated with each topic, discussing the related challenges and data needs. The outcomes were reported back to the full group. Because of the complexity of the topics, most groups first identified a network of value chains and then narrowed the scope to develop a single value chain to address their group’s topic. Although they worked separately and on different topics, the groups came to similar conclusions, concurring that the value chain and decision-tree frameworks are very effective for informing quantitative impact assessments and developing a relatable narrative to assist the public in understanding the link between EO and citizens.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191033","collaboration":"Prepared in cooperation with the National Oceanic and Atmospheric Administration, FourBridges, European Space Agency, and European Association of Remote Sensing Companies","usgsCitation":"Pearlman, F., Lawrence, C.B., Pindilli, E.J., Geppi, D., Shapiro, C.D., Grasso, M., Pearlman, J., Adkins, J., Sawyer, G., and Tassa, A., 2019, Demonstrating the value of Earth observations—Methods, practical applications, and solutions—Group on Earth Observations side event proceedings: U.S. Geological Survey Open-File Report 2019–1033, 33 p., https://doi.org/10.3133/ofr20191033.","productDescription":"vi, 33 p.","numberOfPages":"44","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-102614","costCenters":[{"id":554,"text":"Science and Decisions Center","active":true,"usgs":true}],"links":[{"id":363044,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1033/coverthb.jpg"},{"id":363045,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1033/ofr20191033.pdf","text":"Report","size":"1.22 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1033"}],"contact":"Science and Decisions Center
U.S. Geological Survey
913 National Center
12201 Sunrise Valley Drive
Reston, VA 20192
Email: gs_emeh_sdc@usgs.gov
Hurricane Irma skirted the northern coasts of the U.S. Virgin Islands and Puerto Rico, with maximum sustained winds of 185 miles per hour (mi/h) on September 6, 2017. The hurricane first made landfall in Florida near Cudjoe Key, in the lower Florida Keys, with maximum sustained winds of 130 mi/h on September 10, 2017. The hurricane made a second Florida landfall on Marco Island, Florida, with maximum sustained winds of 115 mi/h on September 10, 2017. The U.S. Geological Survey (USGS), in cooperation with Federal Emergency Management Agency, deployed a temporary monitoring network of water-level and barometric pressure sensors at 249 locations along the Puerto Rico, Florida, Georgia, and South Carolina coasts to record the timing, areal extent, and magnitude of hurricane storm tide and coastal flooding generated by the hurricane. Immediately following the passage of Hurricane Irma, the sensors were retrieved, and the data were disseminated on the USGS Flood Event Viewer (https://stn.wim.usgs.gov/FEV/#IrmaSeptember2017). The storm-tide peak data values were verified by comparing data from hydrologic recorders and nearby high-water marks (HWMs). Following the hurricane, 508 independent HWM locations were flagged and surveyed relative to the North American Vertical Datum of 1988, National Geodetic Vertical Datum of 1929, or a local datum along the southeastern U.S. coast, and to Puerto Rico Vertical Datum of 2002 in Puerto Rico. Most HWMs were in Florida because of the path of the hurricane. The data from the Hurricane Irma storm-tide network are available on a provisional basis in tab-delimited, American Standard Code for Information Interchange (ASCII) format and Network Common Data Form (NetCDF) format by site for each sensor by using the USGS Flood Event Viewer.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191013","collaboration":"Prepared in cooperation with the Federal Emergency Management Agency","usgsCitation":"Byrne, M.J., Sr., and Dickman, M.R., 2019, Monitoring storm tide and flooding from Hurricane Irma along the U.S. Virgin Islands, Puerto Rico, and the Southeastern United States, September 2017 (ver. 1.1, July 2019): U.S. Geological Survey Open-File Report 2019–1013, 35 p., https://doi.org/10.3133/ofr20191013.","productDescription":"vi, 35 p.","numberOfPages":"46","onlineOnly":"N","ipdsId":"IP-095711","costCenters":[{"id":27821,"text":"Caribbean-Florida Water Science Center","active":true,"usgs":true}],"links":[{"id":365693,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1013/ofr20191013.pdf","text":"Report","size":"9.26 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019–1013"},{"id":365694,"rank":2,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2019/1013/versionHist.txt","text":"Version History","size":"1.00 kB","linkFileType":{"id":2,"text":"txt"},"description":"OFR 2019–1013 Version History"},{"id":365697,"rank":3,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1013/coverthb2.jpg"}],"country":"United States","otherGeospatial":"Puerto Rico, U.S. Virgin Islands","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.9892578125,\n 24.00632619875113\n ],\n [\n -79.4970703125,\n 24.00632619875113\n ],\n [\n -79.4970703125,\n 32.0639555946604\n ],\n [\n -88.9892578125,\n 32.0639555946604\n ],\n [\n -88.9892578125,\n 24.00632619875113\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -68.02734375,\n 16.04581345375217\n ],\n [\n -63.45703124999999,\n 16.04581345375217\n ],\n [\n -63.45703124999999,\n 20.96143961409684\n ],\n [\n -68.02734375,\n 20.96143961409684\n ],\n [\n -68.02734375,\n 16.04581345375217\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: April 16, 2019; Version 1.1: July 25, 2019 ","contact":"Director, Caribbean-Florida Water Science Center
U.S. Geological Survey
4446 Pet Lane, Suite 108
Lutz, FL 33559
The U.S. Geological Survey, in cooperation with the Idaho Department of Environmental Quality, sampled groundwater from 15 wells during spring 2018 near the city of Marsing in rural northwestern Owyhee County, southwestern Idaho. Samples were analyzed for field parameters, nutrients, trace elements, major inorganics, and dissolved gas, including methane. To examine trends in individual wells and in the region, ammonia and nitrate results from the spring 2018 sampling were compared with data collected from 1996 to 2015 by the Idaho Department of Environmental Quality and the Idaho State Department of Agriculture.
Fourteen of the 15 samples collected in 2018 contained arsenic (0.13–33.8 micrograms per liter [μg/L]), with 7 arsenic concentrations greater than the U.S. Environmental Protection Agency (EPA) maximum contaminant level (MCL) of 10 μg/L. Iron (465–4,180 μg/L), manganese (54–693 μg/L), sulfate (300–624 milligrams per liter [mg/L]), and total dissolved solids (511–1,350 mg/L) were detected at concentrations greater than EPA secondary maximum contaminant levels (SMCL) in water-quality samples from 6, 10, 4, and 14 of the 15 wells, respectively. Fourteen of the 15 samples contained ammonia concentrations from 0.12 to 7.34 milligrams per liter (mg/L). Six samples contained nitrate concentrations from 0.08 to 24.6 mg/L, with one sample greater than the EPA MCL of 10 mg/L for drinking water. The presence of both ammonia and nitrate in four samples indicated multiple nutrient and groundwater sources and varying redox states. Ammonia concentrations tended to increase downgradient throughout the study area.
Nutrient trend analysis identified water-quality samples from 2 of the 15 wells with increasing nitrate concentrations from 1999–2018 and 2005–2018. The well with increasing nitrate concentrations from 2005–2018 showed a decreasing trend in ammonia concentrations during the same time period. Groundwater-quality samples from the 13 remaining wells showed no temporal trends. A Regional Kendall test, which evaluates trends at numerous wells across the study area to determine if a consistent trend exists for the area, was done to analyze 539 ammonia concentrations from 91 wells over 20 years (1999–2018) and 591 nitrate concentrations from 107 wells over 23 years (1996–2018). The Regional Kendall Test for ammonia had a tau correlation coefficient of -0.073 with a p-value of 0.072, and nitrate had a tau correlation coefficient of -0.041 with a p-value of 0.198, both indicating no statistically significant trends.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191032","collaboration":"Prepared in cooperation with the Idaho Department of Environmental Quality","usgsCitation":"Skinner, K.D., 2019, Groundwater quality and nutrient trends near Marsing, southwestern Idaho, 2018: U.S. Geological Survey Open-File Report 2019-1032, 23 p., https://doi.org/10.3133/ofr20191032.","productDescription":" iv, 24 p.","numberOfPages":"32","onlineOnly":"Y","ipdsId":"IP-095202","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"links":[{"id":362894,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1032/ofr20191032.pdf","text":"Report","size":"27.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1032"},{"id":362893,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1032/coverthb.jpg"}],"country":"United States","state":"Idaho","otherGeospatial":"Marsing","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -116.94345474243164,\n 43.523410314985455\n ],\n [\n -116.7856979370117,\n 43.523410314985455\n ],\n [\n -116.7856979370117,\n 43.60687218565255\n ],\n [\n -116.94345474243164,\n 43.60687218565255\n ],\n [\n -116.94345474243164,\n 43.523410314985455\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Idaho Water Science Center
U.S. Geological Survey
230 Collins Rd
Boise, Idaho 83702-4520
The goal of this research was to evaluate the impacts of Hurricane Sandy on surface elevation trends in estuarine marshes located across the northeast region of the United States from Virginia to Maine using data from an opportunistic (in other words, not strategic) and collaborative network (from here on, an opportunistic network) of surface elevation table-marker horizon (SET-MH) stations. First, we built a data-base of metadata for 965 individual stations from 96 unique geographical locations that included the location, geomorphic setting, and wetland type for each SET-MH station. The dominant estuarine settings included in the analyses were back-barrier lagoonal marshes and emergent marshes along embayments and tidal tributaries. We then calculated prestorm elevation trends to compare to poststorm elevation measurements to determine the storm impact on each station trend. We hypothesized that the effect of Hurricane Sandy on marsh elevation trends would differ by position relative to landfall (right or left) and distance from landfall in southern New Jersey, as both of these variables influence the presence or absence of storm surge as a result of the physical characteristics of tropical cyclones (in other words, strongest winds typically occur to the right of landfall). Storm surge was spatially less extensive and less deep (~1 meter [m]) in marshes located to the left (in other words, south) of landfall compared to marshes located to the right (in other words, north) of landfall where storm surge covered a larger area and was deeper (3–4 m). About 63 percent of 223 eligible stations had a poststorm trend that was similar to the prestorm trend (in other words, less than ±5 millimeters [mm]), indicating little storm impact on elevation trends at those sites. The remaining 37 percent of stations exhibited significant poststorm deviations from the prestorm trend (in other words, greater than ±5 mm). Of these, stations located to the left of landfall had a significant and greater deviation in their elevation trend, and the deviation was more likely to be positive (elevation gain) compared to marshes located to the right of landfall, which had a significant deviation in their elevation trend that was more likely to be negative (elevation loss). This finding is directly related to storm surge impacts on marsh sediment deposition, where deep storm surge (3–4 m) results in sediment deposition in habitats inland of coastal marshes but less so in the marshes themselves. Substrate compaction by the storm surge over-burden may have contributed to elevation loss, but this was not measured because sufficient marker horizon data were not available for analysis. In contrast, to the left of landfall the wind-driven flooding of sediment laden water pushed into the headwaters of rivers and small bays with an ~1 m surge, and resulted in more prevalent sediment deposition on the marsh surfaces and elevation gain. In general, the findings support previous research showing that the physical characteristics of the storm (for example, wind speed, storm surge height, impact angle of landfall) combined with the local wetland conditions (for example, marsh productivity, groundwater level, tide height) are important factors determining a storm’s impact on soil elevation, and that the soil elevation response can vary widely among multiple wetland sites impacted by the same storm and among different storms for the same wetland site.
The final objective of this project was to create a framework using metadata from the opportunistic network of SET-MH stations that could be used to develop a strategic monitoring network designed to address specific climate change impacts and related phenomena identified by land managers and stakeholders. We evaluated the spatial distribution and density of SET-MH stations in relation to geographic coverage, marsh setting, availability of public land, and historical storm surge footprints and hurricane return intervals in order to identify gaps in our understanding of risk and our ability to assess it. Analyses revealed that the general geographic coverage of SET-MH stations is limited given the low percentage of marsh patches with stations, low density of stations, the clumped distribution of stations, and the often limited and uneven distribution of stations in wetlands with a high historical frequency of hurricane strikes and storm surge impacts. These findings can be used by managers and planners to inform the creation of a strategic monitoring network that can, in turn, inform management and adaptation plans for coastal resources in the region. Final plan designs will need to consider financial and infrastructural support required for station maintenance, as well as data collection and management over the long term.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181142","usgsCitation":"Cahoon, D.R., Olker, J.H., Yeates, A.G., Guntenspergen, G.R., Grace, J.B., Adamowicz, S.C., Anisfeld, S., Baldwin, A.H., Barrett, N., Beckett, L., Benzecry, A., Blum, L.K., Burdick, D.M., Crouch, W., Ekberg, M.C., Fernald, S., Grimes, K.W., Grzyb, J., Hartig, E.K., Kreeger, D.A., Larson, M., Lerberg, S., Lynch, J.C., Maher, N., Maxwell-Doyle, M., Mitchell, L.R., Mora, J., O’Neill, V., Padeletti, A., Prosser, D., Quirk, T., Raposa, K.B., Reay, W.G., Siok, D., Snow, C., Starke, A., Staver, L., Stevenson, J.C., and Turner, V., 2019, Hurricane Sandy impacts on coastal wetland resilience: U.S. Geological Survey Open-File Report 2018–1142, 117 p., https://doi.org/10.3133/ofr20181142.","productDescription":"xii, 117 p.","ipdsId":"IP-089853","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":362852,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1142/ofr20181142.pdf","text":"Report","size":"30.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1142"},{"id":362851,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1142/coverthb1.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.251953125,\n 17.811456088564483\n ],\n [\n -70.9716796875,\n 17.811456088564483\n ],\n [\n -70.9716796875,\n 41.07935114946899\n ],\n [\n -93.251953125,\n 41.07935114946899\n ],\n [\n -93.251953125,\n 17.811456088564483\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Eastern Ecological Science Center
U.S. Geological Survey
12311 Beech Forest Road
Laurel, MD 20708
Adapting cultural resources to climate-change effects challenges traditional cultural resource decision making because some adaptation strategies can negatively affect the integrity of cultural resources. Yet, the inevitability of climate-change effects—even given the uncertain timing of those effects—necessitates that managers begin prioritizing resources for climate-change adaptation. Prioritization imposes an additional management challenge: managers must make difficult tradeoffs to achieve desired management outcomes related to maximizing the resource values. This report provides an overview of a pilot effort to integrate vulnerability (exposure and sensitivity), significance, and use potential metrics in a decision framework—the Optimal Preservation (OptiPres) Model—to inform climate adaptation planning of a subset of buildings in historic districts (listed on the National Register of Historic Places) at Cape Lookout National Seashore. The OptiPres Model uses a numerical optimization algorithm to assess the timing and application of a portfolio of adaptation actions that could most effectively preserve an assortment of buildings associated with different histories, intended uses, and construction design and materials over a 30-year planning horizon. The outputs from the different budget scenarios, though not prescriptive, provide visualizations of and insights to the sequence and type of optimal actions and the changes to individual building resource values and accumulated resource values. Study findings suggest the OptiPres Model has planning utility related to fiscal efficiency by identifying a budget threshold necessary to maintain the historical significance and use potential of historical buildings while reducing vulnerability (collectively, the accumulated resource value). Specifically, findings identify that a minimum of the industry standard ($222,000 annually for the 17 buildings) is needed to maintain the current accumulated resource value. Additionally, results suggest that additional appropriations provided on regular intervals when annual appropriations are at the industry standard are nearly as efficient as annual appropriations at twice the rate of industry standards and increase the amount of accumulated resource values to nearly the same level. However, periodic increases in funding may increase the risks posed to buildings from the probability of a natural hazard (that is, damage or loss from a hurricane). Suggestions for model refinements include developing standardized cost estimations for adaptation actions based on square footage and building materials, developing metrics to quantify the historical integrity of buildings, integrating social values data, including additional objectives (such as public safety) in the model, refining vulnerability data and transforming the data to include risk assessment, and incorporating stochastic events (that is, hurricane and wind effects) into the model.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181180","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Seekamp, E., Post van der Burg, M., Fatorić, S., Eaton, M.J., Xiao, X., and McCreary, A., 2019, Optimizing historical preservation under climate change—An overview of the optimal preservation model and pilot testing at Cape Lookout National Seashore: U.S. Geological Survey Open-File Report 2018–1180, 46 p., https://doi.org/10.3133/ofr20181180.","productDescription":"vii, 46 p.","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-096582","costCenters":[{"id":565,"text":"Southeast Climate Science Center","active":true,"usgs":true}],"links":[{"id":362669,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1180/ofr20181180.pdf","text":"Report","size":"4.45 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1180"},{"id":362668,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1180/coverthb.jpg"}],"country":"United States","state":"North Carolina","otherGeospatial":"Cape Lookout National Seashore","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.66397094726562,\n 34.699848377328934\n ],\n [\n -76.67770385742188,\n 34.67274685882317\n ],\n [\n -76.53213500976562,\n 34.557466483188996\n ],\n [\n -76.02264404296875,\n 35.06147690849717\n ],\n [\n -76.0638427734375,\n 35.09519259251624\n ],\n [\n -76.53076171875,\n 34.66597009307397\n ],\n [\n -76.66397094726562,\n 34.699848377328934\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"National Climate Adaptation Science Center
U.S. Geological Survey
12201 Sunrise Valley Drive, Mail Stop 516
Reston, VA 20192
Email: casc@usgs.gov
The Point-Source Load Estimation Tool (PSLoadEsT) provides a user-friendly interface for generating reproducible load calculations for point source dischargers while managing common data challenges including duplicates, incompatible input tables, and incomplete or missing nutrient concentration or effluent flow data. Maintaining a consistent method across an entire study area is important when estimating loads to be used as calibration data for regional water-quality models. PSLoadEsT is written using the open-source programming language R and has an easy-to-use interface written in Visual Basic for Applications® within a Microsoft Access® database file that guides the user through the necessary steps to estimate point source loads. The purpose of this report is to provide a detailed user guide for PSLoadEsT.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191025","collaboration":"National Water Quality Assessment Program","usgsCitation":"Gorman Sanisaca, L.E., Skinner, K.D., and Maupin, M.A., 2019, Annual wastewater nutrient data preparation and load estimation using the Point Source Load Estimation Tool (PSLoadEsT): U.S. Geological Survey Open-File Report 2019-1025, 48 p., https://doi.org/10.3133/ofr20191025.","productDescription":"Report: vi, 48 p.; Additional Report Piece","onlineOnly":"Y","ipdsId":"IP-099356","costCenters":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"links":[{"id":437510,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9QWVZ4L","text":"USGS data release","linkHelpText":"Point-Source Load Estimation Tool (PSLoadEsT)"},{"id":362728,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1025/coverthb.jpg"},{"id":362733,"rank":4,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://doi.org/10.5066/P9QWVZ4L","text":"PSLoadEsT Software release","description":"PSLoadEsT Software release"},{"id":362729,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1025/ofr20191025.pdf","text":"Report","size":"1.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1025"},{"id":362732,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ds1101","text":"Data Series 1101","description":"Data Series 1101","linkHelpText":"Point-Source Nutrient Loads to Streams of the Conterminous United States, 2012"}],"contact":"Director, MD-DE-DC Water Science Center
U.S. Geological Survey
5522 Research Park Drive
Catonsville, MD 21228
Situated in the Mississippi Alluvial Plain of the Gulf Coast Prairie Landscape Conservation Cooperative (GCP LCC), the Chitimacha Tribe is one of four federally recognized tribes in Louisiana. The Tribal seat, trust lands/ reservation, and adjacent Tribal owned lands are located near Charenton, Louisiana, totaling nearly 1,000 acres. The Chitimacha, with a population of approximately 1,400 people, are currently impacted by storm surge, which is expected to increase with climate change. The additional stress from storms will likewise increase the vulnerability to catastrophic impact in the event of a breach in the Atchafalaya Basin Spillway levee. A collaborative effort between the U.S. Geological Survey (USGS) and the Chitimacha Tribe has been initiated to provide resources and expertise to increase the Tribe’s ability to prevent, plan, and prepare for these environmental challenges. By enhancing technical skills, providing access to environmental data, and increasing awareness of environmental issues, the Chitimacha will be better prepared to plan and adapt to the environmental impacts facing their lands related to land use and climate change.
For this project, USGS researchers asked how Chitimacha Tribal Lands might be impacted by future sea level rise scenario projections. These models illustrate some flooding within the northernmost boundary of Chitimacha Tribal Lands.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191030","collaboration":"Prepared in cooperation with The Chitimacha Tribe of Louisiana; Gulf Coast Prairie Landscape Conservation Cooperative","usgsCitation":"Spear, K.A., Jones, W., Griffith, K., Tirpak, B.E., and Walden, K., 2019, Potential sea level rise on Chitimacha Tribal Lands in Louisiana: U.S. Geological Survey Open-File Report 2019–1030, 1 sheet, https://doi.org/10.3133/ofr20191030.","productDescription":"1 Sheet: 24.0 x 36.0 inches","onlineOnly":"Y","ipdsId":"IP-090045","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":502765,"rank":3,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_108491.htm","linkFileType":{"id":5,"text":"html"}},{"id":362387,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1030/ofr20191030.pdf","text":"Report","size":"1.88 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019–1030"},{"id":362386,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1030/coverthb.jpg"}],"country":"United States","state":"Louisiana","otherGeospatial":"Chitimacha Tribal Lands","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -91.5833,\n 29.8167\n ],\n [\n -91.5,\n 29.8167\n ],\n [\n -91.5,\n 29.9167\n ],\n [\n -91.5833,\n 29.9167\n ],\n [\n -91.5833,\n 29.8167\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Wetland and Aquatic Research Center
U.S. Geological Survey
700 Cajundome Blvd.
Lafayette, LA 70506